{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"classification_images_signal_embed_noise.ipynb","provenance":[{"file_id":"1XzhorsaQwoUvMJyohuI8wZwoYLsTbSsB","timestamp":1564870879084}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"CcDFB3Bk55Ey","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"0c21950a-48be-4d4c-a156-76bdb5c03eb0","executionInfo":{"status":"ok","timestamp":1569341604125,"user_tz":240,"elapsed":2398,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}}},"source":["%pylab inline\n","import numpy as np\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torch.utils.data.dataloader as dataloader\n","import torch.optim as optim\n","\n","from torch.utils.data import TensorDataset\n","from torch.autograd import Variable\n","from torchvision import transforms\n","from torchvision.datasets import MNIST\n","import os\n","\n","SEED = 1\n","\n","# CUDA?\n","cuda = torch.cuda.is_available()\n","\n","# For reproducibility\n","torch.manual_seed(SEED)\n","\n","if cuda:\n","    torch.cuda.manual_seed(SEED)"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Populating the interactive namespace from numpy and matplotlib\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"eqmI-pC_ws23","colab_type":"code","outputId":"7293f4b4-fce2-42a5-ef02-b039fea3ba95","executionInfo":{"status":"ok","timestamp":1569271167700,"user_tz":240,"elapsed":3407,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# ls('./data')\n","\n","# import os\n","# os.getcwd()\n","\n","# pwd\n","\n","!ls ./data/MNIST/raw/train-images-idx3-ubyte"],"execution_count":2,"outputs":[{"output_type":"stream","text":["ls: cannot access './data/MNIST/raw/train-images-idx3-ubyte': No such file or directory\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gLHCEEBp5-wH","colab_type":"code","outputId":"fe118207-3437-4d41-8f53-8b3909d800ed","executionInfo":{"status":"ok","timestamp":1569341631744,"user_tz":240,"elapsed":11773,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":255}},"source":["train = MNIST('./data', train=True, download=True, transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","test = MNIST('./data', train=False, download=True, transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","# Create DataLoader\n","dataloader_args = dict(shuffle=True, batch_size=256,num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n","train_loader = dataloader.DataLoader(train, **dataloader_args)\n","test_loader = dataloader.DataLoader(test, **dataloader_args)"],"execution_count":2,"outputs":[{"output_type":"stream","text":["\r0it [00:00, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["9920512it [00:08, 1235798.21it/s]                             \n"],"name":"stderr"},{"output_type":"stream","text":["Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["\r0it [00:00, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["32768it [00:00, 57421.12it/s]                           \n","0it [00:00, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n","Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["1654784it [00:01, 973430.72it/s]                             \n","0it [00:00, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n","Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["8192it [00:00, 21659.32it/s]            "],"name":"stderr"},{"output_type":"stream","text":["Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n","Processing...\n","Done!\n"],"name":"stdout"},{"output_type":"stream","text":["\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"R1G0kvUWn2IH","colab_type":"code","colab":{}},"source":["# dataloader_args\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"l8P08HhX6GHT","colab_type":"code","colab":{}},"source":["# One hidden Layer NN\n","class Model(nn.Module):\n","    def __init__(self):\n","        super(Model, self).__init__()\n","        self.fc = nn.Linear(784, 1000)\n","        self.fc2 = nn.Linear(1000, 10)\n","\n","    def forward(self, x):\n","        x = x.view((-1, 784))\n","        h = F.relu(self.fc(x))\n","        h = self.fc2(h)\n","        return F.softmax(h)    \n","    \n","    \n","model = Model()\n","if cuda:\n","    model.cuda() # CUDA!\n","optimizer = optim.Adam(model.parameters(), lr=1e-3)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"p80g2vi4XZ-y","colab_type":"text"},"source":["**Begining of the signal in NOISE analysis**"]},{"cell_type":"code","metadata":{"id":"B5ocCiLObe3c","colab_type":"code","outputId":"4c94c708-6981-40da-dd3f-48dc14e4cca9","executionInfo":{"status":"ok","timestamp":1569341637759,"user_tz":240,"elapsed":15012,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":85}},"source":["%pylab inline\n","import numpy as np\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torch.utils.data.dataloader as dataloader\n","import torch.optim as optim\n","\n","from torch.utils.data import TensorDataset\n","from torch.autograd import Variable\n","from torchvision import transforms\n","from torchvision.datasets import MNIST\n","import os\n","\n","SEED = 1\n","\n","# CUDA?\n","cuda = torch.cuda.is_available()\n","\n","# For reproducibility\n","torch.manual_seed(SEED)\n","\n","if cuda:\n","    torch.cuda.manual_seed(SEED)\n","\n","# One hidden Layer NN\n","class Model(nn.Module):\n","    def __init__(self):\n","        super(Model, self).__init__()\n","        self.fc = nn.Linear(784, 1000)\n","        self.fc2 = nn.Linear(1000, 1)\n","\n","    def forward(self, x):\n","        x = x.view((-1, 784))\n","        h = F.relu(self.fc(x))\n","        h = F.sigmoid(self.fc2(h))\n","        return h\n","#         return F.softmax(h)    \n","      \n","    \n","    \n","model = Model()\n","if cuda:\n","    model.cuda() # CUDA!\n","optimizer = optim.Adam(model.parameters(), lr=1e-3)"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Populating the interactive namespace from numpy and matplotlib\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['test']\n","`%matplotlib` prevents importing * from pylab and numpy\n","  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"KA_S0ANB2y9y","colab_type":"code","colab":{}},"source":["# One hidden Layer NN\n","class Model(nn.Module):\n","    def __init__(self):\n","        super(Model, self).__init__()\n","        self.fc = nn.Linear(784, 1000)\n","        self.fc2 = nn.Linear(1000, 2)\n","\n","    def forward(self, x):\n","        x = x.view((-1, 784))\n","        h = F.relu(self.fc(x))\n","        h = self.fc2(h)\n","        return F.softmax(h, dim=1)    \n","    \n","    \n","model = Model()\n","if cuda:\n","    model.cuda() # CUDA!\n","optimizer = optim.Adam(model.parameters(), lr=1e-3)\n","\n","\n","\n","\n","\n","#  CNN\n","class Model(nn.Module):\n","    def __init__(self):\n","        super(Model, self).__init__()\n","        self.conv1 = nn.Conv2d(1, 40, 5, 1)\n","        self.conv2 = nn.Conv2d(40, 50, 5, 1)\n","        self.fc1 = nn.Linear(4*4*50, 100)\n","        self.fc2 = nn.Linear(100, 1)\n","\n","    def forward(self, x):\n","        x = F.relu(self.conv1(x))\n","        x = F.max_pool2d(x, 2, 2)\n","        x = F.relu(self.conv2(x))\n","        x = F.max_pool2d(x, 2, 2)\n","        x = x.view(-1, 4*4*50)\n","        x = F.relu(self.fc1(x))\n","#         x = self.fc2(x)\n","#         return F.softmax(x, dim=1)\n","        h = F.sigmoid(self.fc2(x))\n","        return h\n","\n","    \n","model = Model()\n","if cuda:\n","    model.cuda() # CUDA!\n","optimizer = optim.Adam(model.parameters(), lr=1e-3) \n","    \n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"TePq1ktAWdr1","colab_type":"code","colab":{}},"source":["# # A binary classifier\n","\n","# from torchvision import datasets, transforms\n","\n","# def mnist_data():\n","#     compose = transforms.Compose(\n","#         [\n","#          transforms.ToTensor(),\n","#          transforms.Normalize((.5), (.5))         \n","# #          transforms.Normalize((.5, .5, .5), (.5, .5, .5))\n","#         ])\n","# #     transform = transforms.Compose([transforms.Resize(64),   # scale!!!!!\n","# #         transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])    \n","\n","#     transform = transforms.Compose([\n","#         transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])    \n","\n","    \n","#     out_dir = './dataset'\n","#     return datasets.MNIST(root=out_dir, train=True, transform=transform, download=True)\n","\n","\n","\n","train = MNIST('./data', train=True,   download=True\n","    , transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","test = MNIST('./data', train=False, download=True , transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","  \n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"h9D9gNczXhFd","colab_type":"code","outputId":"3f91cfd8-3ae8-4df3-dbcb-7c5a39715bb2","executionInfo":{"status":"ok","timestamp":1569341668605,"user_tz":240,"elapsed":342,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Load data\n","\n","# digit_p, digit_q =  2, 7\n","\n","# digit_p, digit_q =  4,9\n","digit_p, digit_q =  5,6\n","\n","# digit_p, digit_q =  1,7\n","\n","\n","data1 = train\n","# selecting number 0 zero only\n","tt = data1.targets[(data1.targets== digit_p) | (data1.targets== digit_q)]\n","tt[tt==digit_p] =  0\n","tt[tt==digit_q] = 1\n","dd = data1.data[(data1.targets== digit_p) | (data1.targets== digit_q)] \n","# tt = data.targets[(data.targets== 1)]\n","# dd = data.data[(data.targets== 1)] \n","\n","data1.targets = tt\n","data1.data = dd\n","train_loader = torch.utils.data.DataLoader(data1, batch_size=100, shuffle=True, drop_last = True)\n","# Num batches\n","# num_batches = len(train_loader)\n","\n","print((tt==0).sum(), (tt==1).sum())\n","\n","\n","\n","\n","\n","data2 = test\n","# selecting number 0 zero only\n","tt = data2.targets[(data2.targets== digit_p) | (data2.targets== digit_q)]\n","tt[tt==digit_p] =  0\n","tt[tt==digit_q] = 1\n","dd = data2.data[(data2.targets== digit_p) | (data2.targets== digit_q)] \n","# tt = data.targets[(data.targets== 1)]\n","# dd = data.data[(data.targets== 1)] \n","\n","data2.targets = tt\n","data2.data = dd\n","test_loader = torch.utils.data.DataLoader(data2, batch_size=100, shuffle=True, drop_last = True)\n","# Num batches\n","# num_batches = len(train_loader)\n","print((tt==0).sum(), (tt==1).sum())"],"execution_count":10,"outputs":[{"output_type":"stream","text":["tensor(5421) tensor(5918)\n","tensor(892) tensor(958)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"kOGtuG9dXg9q","colab_type":"code","outputId":"b8d597f9-2ca5-47bd-f738-341d85267585","executionInfo":{"status":"ok","timestamp":1569272052624,"user_tz":240,"elapsed":736,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# a = (data.targets== 1) | (data.targets== 2)\n","# num_batches\n","# target.cuda()\n","# (test.targets==-1).sum()\n","# (data.targets==5).sum()\n","# len(tt)\n","# (tt==0).sum()\n","\n","\n","from google.colab import drive\n","drive.mount('/content/drive/')"],"execution_count":18,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"1Fq_AfTgiKPQ","colab_type":"code","colab":{}},"source":["# data.targets[100]\n","a = torch.ByteTensor([0,1,1,0])\n","b = torch.ByteTensor([0,1,0,1])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ayK0_kfdi8Vx","colab_type":"code","outputId":"37a8d164-992c-4e7a-dcdd-6007619745ed","executionInfo":{"status":"error","timestamp":1569257908771,"user_tz":240,"elapsed":1276,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":198}},"source":["a.dtype\n","a | b\n","(data.targets==1) "],"execution_count":0,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-135-2755e12882f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mAttributeError\u001b[0m: 'Tensor' object has no attribute 'targets'"]}]},{"cell_type":"code","metadata":{"id":"r6UeyphIjeTp","colab_type":"code","colab":{}},"source":["# data.data.shape\n","\n","\n","def one_hot(target):\n","#   one hot encoding\n","  batch_size = target.size(0)\n","  nb_digits = 2\n","  # Dummy input that HAS to be 2D for the scatter (you can use view(-1,1) if needed)\n","  # y = torch.LongTensor(target.size(0),1).random_() % nb_digits\n","  # One hot encoding buffer that you create out of the loop and just keep reusing\n","  y_onehot = torch.FloatTensor(batch_size, nb_digits)\n","\n","  # In your for loop\n","  y_onehot.zero_()\n","  y_onehot.scatter_(1, target[:,None].type(torch.LongTensor), 1)\n","  target = y_onehot\n","  \n","  return target\n","\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"KrLEw8OibWdd","colab_type":"code","outputId":"6ba71395-76b8-4763-cf98-d6b2dd4a4bc7","executionInfo":{"status":"ok","timestamp":1569273923518,"user_tz":240,"elapsed":43882,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":391}},"source":["# GPU compatible\n","EPOCHS = 20\n","losses = []\n","BCE_loss = nn.BCELoss()\n","\n","model.train()\n","for epoch in range(EPOCHS):\n","    for batch_idx, (data, target) in enumerate(train_loader):\n","        # Get Samples\n","#         import pdb; pdb.set_trace()\n","        data, target = Variable(data/256), Variable(target)\n","        \n","        if cuda:\n","            data, target = data.cuda(), target.cuda()\n","        \n","        # Init\n","        optimizer.zero_grad()\n","\n","        # Predict\n","        y_pred = model(data).squeeze(1) #.cuda()\n","\n","        # Calculate loss\n","        target = target.type(torch.FloatTensor)#.cuda()\n","        y_pred = y_pred.type(torch.FloatTensor)#.cuda()\n","\n","#         loss = F.cross_entropy(y_pred, target)\n","        \n","#         target = one_hot(target)\n","\n","\n","#         import pdb; pdb.set_trace()\n","        loss = BCE_loss(y_pred, target)\n","        \n","        losses.append(loss.cpu().data)\n","#         losses.append(loss.cpu().data[0])        \n","        # Backpropagation\n","        loss.backward()\n","        optimizer.step()\n","        \n","        \n","        # Display\n","        if batch_idx % 100 == 1:\n","            print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n","                epoch+1,\n","                EPOCHS,\n","                batch_idx * len(data), \n","                len(train_loader.dataset),\n","                100. * batch_idx / len(train_loader), \n","                loss.cpu().data), \n","                end='')\n","    # Eval\n","#     evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","#     evaluate_y = Variable(test_loader.dataset.test_labels)\n","    evaluate_x = test.data.type(torch.FloatTensor)/256 # Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","    evaluate_y = test.targets #Variable(test_loader.dataset.test_labels)    \n","    \n","#     evaluate_y = one_hot(evaluate_y)\n","    \n","    \n","    if cuda:\n","        evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","    model.eval()\n","    output = model(evaluate_x[:,None,...])\n","#     pred = output.data.max(1)[1]\n","    pred = output.type(torch.ByteTensor).squeeze()\n","    evaluate_y = evaluate_y.type(torch.ByteTensor)\n","\n","    \n","#     import pdb; pdb.set_trace()\n","    d = pred.eq(evaluate_y.data).cpu()\n","\n","#     vals, idxs = pred.max(dim=1)\n","#     vals2, idxs2 = evaluate_y.data.max(dim=1)\n","#     d = idxs.eq(idxs2).cpu()\n","    accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","    \n","    print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\\t Test Accuracy: {:.4f}%'.format(\n","        epoch+1,\n","        EPOCHS,\n","        len(train_loader.dataset), \n","        len(train_loader.dataset),\n","        100. * batch_idx / len(train_loader), \n","        loss.cpu().data,\n","        accuracy*100,\n","        end=''))"],"execution_count":59,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1386: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n","  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"],"name":"stderr"},{"output_type":"stream","text":[" Train Epoch: 1/20 [11339/11339 (99%)]\tLoss: 0.691935\t Test Accuracy: 48.2162%\n"," Train Epoch: 2/20 [11339/11339 (99%)]\tLoss: 0.689214\t Test Accuracy: 48.2162%\n"," Train Epoch: 3/20 [11339/11339 (99%)]\tLoss: 0.257038\t Test Accuracy: 87.7297%\n"," Train Epoch: 4/20 [11339/11339 (99%)]\tLoss: 0.064174\t Test Accuracy: 88.6486%\n"," Train Epoch: 5/20 [11339/11339 (99%)]\tLoss: 0.145774\t Test Accuracy: 89.3514%\n"," Train Epoch: 6/20 [11339/11339 (99%)]\tLoss: 0.079312\t Test Accuracy: 91.0270%\n"," Train Epoch: 7/20 [11339/11339 (99%)]\tLoss: 0.125870\t Test Accuracy: 92.7027%\n"," Train Epoch: 8/20 [11339/11339 (99%)]\tLoss: 0.122381\t Test Accuracy: 94.1081%\n"," Train Epoch: 9/20 [11339/11339 (99%)]\tLoss: 0.098577\t Test Accuracy: 94.6486%\n"," Train Epoch: 10/20 [11339/11339 (99%)]\tLoss: 0.056769\t Test Accuracy: 95.7297%\n"," Train Epoch: 11/20 [11339/11339 (99%)]\tLoss: 0.043718\t Test Accuracy: 96.2703%\n"," Train Epoch: 12/20 [11339/11339 (99%)]\tLoss: 0.034392\t Test Accuracy: 96.3243%\n"," Train Epoch: 13/20 [11339/11339 (99%)]\tLoss: 0.032330\t Test Accuracy: 96.5405%\n"," Train Epoch: 14/20 [11339/11339 (99%)]\tLoss: 0.133898\t Test Accuracy: 96.8649%\n"," Train Epoch: 15/20 [11339/11339 (99%)]\tLoss: 0.073946\t Test Accuracy: 96.9730%\n"," Train Epoch: 16/20 [11339/11339 (99%)]\tLoss: 0.024917\t Test Accuracy: 97.2432%\n"," Train Epoch: 17/20 [11339/11339 (99%)]\tLoss: 0.019444\t Test Accuracy: 97.8378%\n"," Train Epoch: 18/20 [11339/11339 (99%)]\tLoss: 0.013192\t Test Accuracy: 97.7838%\n"," Train Epoch: 19/20 [11339/11339 (99%)]\tLoss: 0.034308\t Test Accuracy: 98.4324%\n"," Train Epoch: 20/20 [11339/11339 (99%)]\tLoss: 0.050496\t Test Accuracy: 98.3784%\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"g5UbVoAb5Fny","colab_type":"code","outputId":"85663d43-d8c6-4caa-f79c-5c0cc43e1451","executionInfo":{"status":"error","timestamp":1569258181517,"user_tz":240,"elapsed":828,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":130}},"source":["y_onehot = torch.FloatTensor(3, 3)\n","y_onehot.zero_()\n","y_onehot.scatter_("],"execution_count":0,"outputs":[{"output_type":"error","ename":"SyntaxError","evalue":"ignored","traceback":["\u001b[0;36m  File \u001b[0;32m\"<ipython-input-163-edb68b0eb6b6>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    y_onehot.scatter_(\u001b[0m\n\u001b[0m                      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected EOF while parsing\n"]}]},{"cell_type":"code","metadata":{"id":"X4mSg4TG92VQ","colab_type":"code","outputId":"b5fbf7de-6d8b-4e91-9708-2096c76adc30","executionInfo":{"status":"ok","timestamp":1569259580802,"user_tz":240,"elapsed":243727,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":561}},"source":["# this worked with TPU\n","EPOCHS = 30\n","losses = []\n","BCE_loss = nn.BCELoss()\n","\n","model.train()\n","for epoch in range(EPOCHS):\n","    for batch_idx, (data, target) in enumerate(train_loader):\n","        # Get Samples\n","#         import pdb; pdb.set_trace()\n","        data, target = Variable(data), Variable(target)\n","        \n","#         if cuda:\n","#             data, target = data.cuda(), target.cuda()\n","        \n","        # Init\n","        optimizer.zero_grad()\n","\n","        # Predict\n","        y_pred = model(data).squeeze(1)\n","\n","        # Calculate loss\n","        target = target.type(torch.FloatTensor)#.cuda()\n","        y_pred = y_pred.type(torch.FloatTensor)#.cuda()\n","\n","#         loss = F.cross_entropy(y_pred, target)\n","        loss = BCE_loss(y_pred, target)\n","        \n","        losses.append(loss.cpu().data)\n","#         losses.append(loss.cpu().data[0])        \n","        # Backpropagation\n","        loss.backward()\n","        optimizer.step()\n","        \n","        \n","        # Display\n","        if batch_idx % 100 == 1:\n","            print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n","                epoch+1,\n","                EPOCHS,\n","                batch_idx * len(data), \n","                len(train_loader.dataset),\n","                100. * batch_idx / len(train_loader), \n","                loss.cpu().data), \n","                end='')\n","    # Eval\n","#     evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","#     evaluate_y = Variable(test_loader.dataset.test_labels)\n","    evaluate_x = test.data.type(torch.FloatTensor) # Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","    evaluate_y = test.targets #Variable(test_loader.dataset.test_labels)    \n","    if cuda:\n","        evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","    model.eval()\n","    output = model(evaluate_x[:,None,...])\n","#     pred = output.data.max(1)[1]\n","    pred = output.type(torch.ByteTensor).squeeze()\n","    evaluate_y = evaluate_y.type(torch.ByteTensor)\n","\n","    d = pred.eq(evaluate_y.data).cpu()\n","    accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","    \n","    print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\\t Test Accuracy: {:.4f}%'.format(\n","        epoch+1,\n","        EPOCHS,\n","        len(train_loader.dataset), \n","        len(train_loader.dataset),\n","        100. * batch_idx / len(train_loader), \n","        loss.cpu().data,\n","        accuracy*100,\n","        end=''))\n","    "],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1386: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n","  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"],"name":"stderr"},{"output_type":"stream","text":[" Train Epoch: 1/30 [13007/13007 (99%)]\tLoss: 0.052242\t Test Accuracy: 99.3528%\n"," Train Epoch: 2/30 [13007/13007 (99%)]\tLoss: 0.002052\t Test Accuracy: 99.6301%\n"," Train Epoch: 3/30 [13007/13007 (99%)]\tLoss: 0.004209\t Test Accuracy: 99.7226%\n"," Train Epoch: 4/30 [13007/13007 (99%)]\tLoss: 0.000672\t Test Accuracy: 99.7688%\n"," Train Epoch: 5/30 [13007/13007 (99%)]\tLoss: 0.017979\t Test Accuracy: 99.8613%\n"," Train Epoch: 6/30 [13007/13007 (99%)]\tLoss: 0.000421\t Test Accuracy: 99.6764%\n"," Train Epoch: 7/30 [13007/13007 (99%)]\tLoss: 0.000106\t Test Accuracy: 99.8613%\n"," Train Epoch: 8/30 [13007/13007 (99%)]\tLoss: 0.013088\t Test Accuracy: 99.7226%\n"," Train Epoch: 9/30 [13007/13007 (99%)]\tLoss: 0.005762\t Test Accuracy: 99.8613%\n"," Train Epoch: 10/30 [13007/13007 (99%)]\tLoss: 0.000750\t Test Accuracy: 99.7688%\n"," Train Epoch: 11/30 [13007/13007 (99%)]\tLoss: 0.003983\t Test Accuracy: 99.7688%\n"," Train Epoch: 12/30 [13007/13007 (99%)]\tLoss: 0.000213\t Test Accuracy: 99.7688%\n"," Train Epoch: 13/30 [13007/13007 (99%)]\tLoss: 0.000198\t Test Accuracy: 99.7688%\n"," Train Epoch: 14/30 [13007/13007 (99%)]\tLoss: 0.000305\t Test Accuracy: 99.7226%\n"," Train Epoch: 15/30 [13007/13007 (99%)]\tLoss: 0.000365\t Test Accuracy: 99.8151%\n"," Train Epoch: 16/30 [13007/13007 (99%)]\tLoss: 0.000035\t Test Accuracy: 99.8151%\n"," Train Epoch: 17/30 [13007/13007 (99%)]\tLoss: 0.019707\t Test Accuracy: 99.8151%\n"," Train Epoch: 18/30 [13007/13007 (99%)]\tLoss: 0.000028\t Test Accuracy: 99.8613%\n"," Train Epoch: 19/30 [13007/13007 (99%)]\tLoss: 0.000063\t Test Accuracy: 99.6764%\n"," Train Epoch: 20/30 [13007/13007 (99%)]\tLoss: 0.000092\t Test Accuracy: 99.8151%\n"," Train Epoch: 21/30 [13007/13007 (99%)]\tLoss: 0.002130\t Test Accuracy: 99.7688%\n"," Train Epoch: 22/30 [13007/13007 (99%)]\tLoss: 0.000030\t Test Accuracy: 99.8151%\n"," Train Epoch: 23/30 [13007/13007 (99%)]\tLoss: 0.000232\t Test Accuracy: 99.8613%\n"," Train Epoch: 24/30 [13007/13007 (99%)]\tLoss: 0.000045\t Test Accuracy: 99.7688%\n"," Train Epoch: 25/30 [13007/13007 (99%)]\tLoss: 0.000070\t Test Accuracy: 99.8151%\n"," Train Epoch: 26/30 [13007/13007 (99%)]\tLoss: 0.000013\t Test Accuracy: 99.7688%\n"," Train Epoch: 27/30 [13007/13007 (99%)]\tLoss: 0.000261\t Test Accuracy: 99.8151%\n"," Train Epoch: 28/30 [13007/13007 (99%)]\tLoss: 0.000004\t Test Accuracy: 99.8151%\n"," Train Epoch: 29/30 [13007/13007 (99%)]\tLoss: 0.000005\t Test Accuracy: 99.8151%\n"," Train Epoch: 30/30 [13007/13007 (99%)]\tLoss: 0.000003\t Test Accuracy: 99.8613%\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"957yl_w9xg3T","colab_type":"code","outputId":"3450fa47-1ca6-49dd-af30-8c0b13f489e5","executionInfo":{"status":"ok","timestamp":1569271279003,"user_tz":240,"elapsed":851,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"source":["evaluate_x = test.data.type(torch.FloatTensor) # Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","evaluate_y = test.targets #Variable(test_loader.dataset.test_labels)\n","if cuda:\n","    evaluate_x, evaluate_y = evaluate_x.cuda()/256, evaluate_y.cuda()\n","\n","model.eval()\n","output = model(evaluate_x[:,None,...])\n","# pred = output.data.max(1)[1]\n","pred = output.type(torch.ByteTensor).squeeze()\n","evaluate_y = evaluate_y.type(torch.ByteTensor)\n","\n","d = pred.eq(evaluate_y.data).cpu()\n","accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","\n","print('Accuracy:', accuracy*100)"],"execution_count":9,"outputs":[{"output_type":"stream","text":["Accuracy: tensor(98.1507, dtype=torch.float64)\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1386: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n","  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"kfe4An50x81x","colab_type":"code","outputId":"3df9c987-7f46-40e5-dc40-a2c392dde79c","executionInfo":{"status":"ok","timestamp":1565982198930,"user_tz":240,"elapsed":1528,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["evaluate_y\n","pred.max()\n","output.min()\n","output.shape\n","pred.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([2163])"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"code","metadata":{"id":"lTLhZx2RnDur","colab_type":"code","outputId":"6a49f2b3-c1ee-472a-fefe-b24bc28b165d","executionInfo":{"status":"ok","timestamp":1565831703065,"user_tz":240,"elapsed":181,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# data.cpu()\n","# y_pred.squeeze(1).shape\n","e = target.type(torch.BoolTensor)\n","# BCE_loss(e, e)\n","# y_pred.dtype\n","# target\n","y_pred.max()\n","# target.dtype"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(1.0000, grad_fn=<MaxBackward1>)"]},"metadata":{"tags":[]},"execution_count":103}]},{"cell_type":"code","metadata":{"id":"QRD_ehFcbWoF","colab_type":"code","outputId":"1f101004-b0ff-4c2a-aeac-723cf9561a76","executionInfo":{"status":"ok","timestamp":1565929053779,"user_tz":240,"elapsed":154,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["# target[44]\n","d, r = next(iter((train_loader)))\n","r"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0,\n","        1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1,\n","        0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0,\n","        0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0,\n","        0, 1, 0, 1])"]},"metadata":{"tags":[]},"execution_count":202}]},{"cell_type":"code","metadata":{"id":"bLdSc4YIpR1U","colab_type":"code","colab":{}},"source":["test_data = test.data.type(torch.FloatTensor) # Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","test_data = test_data/256\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"gbfQPN72pS_r","colab_type":"code","outputId":"d77a7852-109d-4d5f-cc22-4d085ea03a85","executionInfo":{"status":"error","timestamp":1569341648920,"user_tz":240,"elapsed":570,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":181}},"source":["(test.targets==0).sum()\n","test_data.size(0)"],"execution_count":7,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-7-296c421a286e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtest_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'test_data' is not defined"]}]},{"cell_type":"code","metadata":{"id":"w7vvNfF8bXG9","colab_type":"code","outputId":"ebd7d948-f626-4fb7-8409-9e620dc2b5d1","executionInfo":{"status":"ok","timestamp":1569341695288,"user_tz":240,"elapsed":558,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":85}},"source":["# embed the signal in noise\n","\n","# - get a random test digit and add it to random noise\n","# - feed to the net and collect the answer\n","# - do record the averages\n","\n","\n","# model.cuda()\n","model.eval()\n","test_data = test.data.type(torch.FloatTensor) # Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","test_data = test_data/256\n","\n","\n","#     output = model(evaluate_x[:,None,...])\n","\n","\n","\n","batch_size = test_data.size(0)\n","all_size = batch_size * 1\n","\n","stats = dict()\n","for i in range(2):\n","    stats[i] = 0\n","\n","iters = 1    \n","    \n","avgs = torch.zeros(iters, 2, 28*28)\n","\n","\n","weight = 100\n","\n","for kk in range(iters):\n","  print(kk)\n","  \n","#   z = torch.rand(all_size, 1, 28, 28)*2 -1 #.cuda()\n","  z = torch.rand(all_size, 28, 28) #.cuda()  \n","  z.cuda()\n","  #plt.imshow(z[1].reshape(28,28))\n","\n","\n","  all_preds = []\n","  all_idx = torch.ones(all_size, dtype = torch.uint8)\n","  for k in range(0, all_size, batch_size):\n","#       stimulus = z[k:k+batch_size] + test_data  # noise + stim\n","#       kk = (test_data[0] + 5*z[0])/5\n","      stimulus = (1*z[k:k+batch_size] + weight*test_data)/ (weight+1)  # noise + stim\n","        \n","      y_pred = model(test_data[:,None,...].cuda())\n","      \n","      conf, pred = y_pred.data.max(1)\n","#       y_pred[y_pred < 0.5]= 0\n","#       y_pred[y_pred > 0.5]= 1      \n","      z[k:k+batch_size] = stimulus\n","\n","#       import pdb; pdb.set_trace()\n","  \n","#       indices = torch.ones(y_pred.size(0), dtype = torch.uint8)\n","#       indices[torch.mean(y_pred, dim =1)==0] = 0 \n","#       import pdb; pdb.set_trace()\n","      all_idx[k:k+batch_size] = pred.squeeze() \n","#       pred = y_pred[indices==1].data.max(1)[1]\n","\n","      all_preds.append(pred)\n","#       all_preds.append(y_pred)\n","\n","  pred = torch.cat(all_preds).squeeze()\n","\n","  for i in range(2):\n","    stats[i] += torch.sum(all_idx==i)\n","  \n","#   z = z[all_idx]\n","  for i in range(2):\n","      a = torch.mean(z[pred==i], dim=0) \n","      avgs[kk, i] = a.reshape(28*28)\n","\n","print(stats)"],"execution_count":11,"outputs":[{"output_type":"stream","text":["0\n","{0: tensor(1850), 1: tensor(0)}\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1386: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n","  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"SGlYJurKJjay","colab_type":"code","colab":{}},"source":["# # embed the signal in noise\n","\n","# # - get a random test digit and add it to random noise\n","# # - feed to the net and collect the answer\n","# # - do record the averages\n","\n","# # Load data\n","\n","# # digit_p, digit_q =  1, 7\n","\n","\n","# test = MNIST('./data', train=False, download=True , transform=transforms.Compose([\n","#     transforms.ToTensor(), # ToTensor does min-max normalization. \n","# ]), )\n","\n","# data2 = test\n","# # selecting number 0 zero only\n","# tt = data2.targets[(data2.targets== digit_p) | (data2.targets== digit_q)]\n","# tt[tt==digit_p] =  0\n","# tt[tt==digit_q] = 1\n","# dd = data2.data[(data2.targets== digit_p) | (data2.targets== digit_q)] \n","# # tt = data.targets[(data.targets== 1)]\n","# # dd = data.data[(data.targets== 1)] \n","\n","# data2.targets = tt\n","# data2.data = dd\n","# test_loader = torch.utils.data.DataLoader(data2, batch_size=100, shuffle=True, drop_last = True)\n","# # Num batches\n","# # num_batches = len(train_loader)\n","# print((tt==0).sum(), (tt==1).sum())\n","\n","# # model.cuda()\n","# model.eval()\n","# test_data = test.data.type(torch.FloatTensor) # Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","# test_data = test_data/256\n","\n","\n","# #     output = model(evaluate_x[:,None,...])\n","\n","# batch_size = test_data.size(0)\n","# all_size = batch_size * 1\n","\n","# stats = dict()\n","# for i in range(2):\n","#     stats[i] = 0\n","\n","# iters = 1    \n","    \n","# avgs = torch.zeros(iters, 2, 28*28)\n","\n","\n","# weight = 0.3\n","\n","# for kk in range(iters):\n","#   print(kk)\n","  \n","#   z = torch.rand(all_size, 28, 28) #.cuda()  \n","#   z.cuda()\n","\n","#   # z = (1*z + weight*test_data)/ (weight+1)  # noise + stim\n","#   z = (1-weight)*z + weight*test_data #/ (weight+1)  # noise + stim\n","\n","#   pred = model(z[:,None,...].cuda())\n","\n","#   for i in range(2):\n","#     stats[i] += torch.sum(pred==i)\n","  \n","#   for i in range(2):\n","#       a = torch.mean(z[pred.squeeze()==i], dim=0) \n","#       avgs[kk, i] = a.reshape(28*28)\n","\n","# print(stats)\n","\n","# # plot the avg\n","# dd = torch.mean(avgs, dim=0)\n","# for kk in range(2):\n","#   fig = plt.figure()\n","#   a = dd[kk]\n","#   a = a.view(-1,28)\n","#   plt.imshow(a, cmap = 'gray')\n","\n","  \n","  \n","# # plot few samples  \n","# for p in range(2):\n","# #   pred.shape\n","#   # z.shape\n","#   z = torch.rand(all_size, 28, 28)\n","\n","#   # plt.imshow(stimulus[1])\n","#   plt.figure()\n","#   kk = (1*z[p] + weight*test_data[p])/ (weight+1)\n","# #   (test_data[110] + 5*z[p])/5\n","\n","#   # plt.imshow(z[k:k+batch_size][0])\n","#   plt.imshow(kk)\n","#   # batch_size\n","#   # z[k:k+batch_size][0].shape\n","#   # z[k:k+batch_size][0].max()\n","#   print(kk.min(), kk.max())\n","#   print(z[p].min(), z[p].max())\n","#   print(test_data[p].min(), test_data[p].max())\n","\n","#   plt.figure()\n","#   plt.imshow(test_data[p])\n","  \n","#   plt.figure()\n","#   plt.imshow(z[p])\n","    "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"4fKFhRK9vtq1","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":530},"outputId":"e3ce7cb3-99cc-455e-e1cd-3343e8219235","executionInfo":{"status":"ok","timestamp":1569341745176,"user_tz":240,"elapsed":30177,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}}},"source":["# embed the signal in noise\n","\n","# - get a random test digit and add it to random noise\n","# - feed to the net and collect the answer\n","# - do record the averages\n","\n","# Load data\n","\n","# digit_p, digit_q =  1, 7\n","\n","\n","test = MNIST('./data', train=False, download=True , transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","data2 = test\n","# selecting number 0 zero only\n","tt = data2.targets[(data2.targets== digit_p) | (data2.targets== digit_q)]\n","tt[tt==digit_p] =  0\n","tt[tt==digit_q] = 1\n","dd = data2.data[(data2.targets== digit_p) | (data2.targets== digit_q)] \n","# tt = data.targets[(data.targets== 1)]\n","# dd = data.data[(data.targets== 1)] \n","\n","data2.targets = tt\n","data2.data = dd\n","test_loader = torch.utils.data.DataLoader(data2, batch_size=100, shuffle=True, drop_last = True)\n","# Num batches\n","# num_batches = len(train_loader)\n","print((tt==0).sum(), (tt==1).sum())\n","\n","# model.cuda()\n","model.eval()\n","test_data = test.data.type(torch.FloatTensor) # Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","test_data = test_data/256\n","\n","\n","\n","f, axarr = plt.subplots(4, 10, )\n","f.set_figheight(7)\n","f.set_figwidth(20)\n","\n","\n","\n","\n","# weight = 0.3\n","# for idx, weight in enumerate([.6, .1, 0.05, 0]):\n","for idx, weight in enumerate([.3, .2, 0.1, 0]):\n","\n","  # plot few samples  \n","  for kk, p in enumerate([1, 1300]): # range(2):\n","  #   pred.shape\n","    # z.shape\n","    z = torch.rand(28, 28)\n","\n","    # plt.imshow(stimulus[1])\n","    # plt.figure()\n","    # kk = (1*z[p] + weight*test_data[p])/ (weight+1)\n","    img = (1-weight)*z + weight*test_data[p] #/ (weight+1)  # noise + stim\n","  #   (test_data[110] + 5*z[p])/5\n","\n","    axarr[idx, kk].imshow(img)    \n","    # if not idx:\n","    #   axarr[idx, 0].set_title(f'predicted: {digit_p} - gt:{digit_p}') \n","    axarr[idx,kk].axis('off')\n","\n","  for kk in range(2,10): # range(2):\n","    axarr[idx, kk].axis('off')\n","\n","    # # plt.imshow(z[k:k+batch_size][0])\n","    # plt.imshow(kk)\n","    # # batch_size\n","    # # z[k:k+batch_size][0].shape\n","    # # z[k:k+batch_size][0].max()\n","    # print(kk.min(), kk.max())\n","    # print(z[p].min(), z[p].max())\n","    # print(test_data[p].min(), test_data[p].max())\n","\n","    # plt.figure()\n","    # plt.imshow(test_data[p])\n","    \n","    # plt.figure()\n","    # plt.imshow(z[p])\n","\n","    plt.tight_layout()\n","\n","\n","    f.subplots_adjust(hspace=0) #, wspace=0.0, right = 0.8)\n","    f.show()\n","\n","    "],"execution_count":12,"outputs":[{"output_type":"stream","text":["tensor(892) 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size 1440x504 with 40 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"62dIMMU3aX3u","colab_type":"code","outputId":"3d8a3c33-b4e2-4939-ecd4-71b7479bf33e","executionInfo":{"status":"ok","timestamp":1569259231596,"user_tz":240,"elapsed":801,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["pred.shape\n","all_size\n","(test_targets == 0).sum() + (test_targets == 1).sum()\n","(pred == 1).sum()\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(986, device='cuda:0')"]},"metadata":{"tags":[]},"execution_count":214}]},{"cell_type":"code","metadata":{"id":"XZD1Imd7B7jQ","colab_type":"code","outputId":"58e63f51-8871-4ca1-81a0-2c706243bf08","executionInfo":{"status":"ok","timestamp":1569257438312,"user_tz":240,"elapsed":919,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["z.min()\n","# test_data.max()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(4.0531e-07)"]},"metadata":{"tags":[]},"execution_count":105}]},{"cell_type":"code","metadata":{"id":"G5L18f6kaFH9","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":638},"outputId":"21ef808c-84a5-4bcb-9174-58b78eb5f673","executionInfo":{"status":"ok","timestamp":1569274098030,"user_tz":240,"elapsed":105450,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}}},"source":["# embed the signal in noise\n","\n","\n","#.  WARNING: for some well-trained classifiers!! it may not produce good results. Try to train another classifier\n","\n","\n","# - get a random test digit and add it to random noise\n","# - feed to the net and collect the answer\n","# - do record the averages\n","\n","# Load data\n","\n","# digit_p, digit_q =  1, 7\n","\n","\n","test = MNIST('./data', train=False, download=True , transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","data2 = test\n","# selecting number 0 zero only\n","tt = data2.targets[(data2.targets== digit_p) | (data2.targets== digit_q)]\n","tt[tt==digit_p] =  0\n","tt[tt==digit_q] = 1\n","dd = data2.data[(data2.targets== digit_p) | (data2.targets== digit_q)] \n","# tt = data.targets[(data.targets== 1)]\n","# dd = data.data[(data.targets== 1)] \n","\n","data2.targets = tt\n","data2.data = dd\n","test_loader = torch.utils.data.DataLoader(data2, batch_size=100, shuffle=True, drop_last = True)\n","# Num batches\n","# num_batches = len(train_loader)\n","print((tt==0).sum(), (tt==1).sum())\n","\n","# model.cuda()\n","model.eval()\n","test_data = test.data.type(torch.FloatTensor) # Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","test_data = test_data/256\n","\n","test_targets = test.targets.cuda()\n","#     output = model(evaluate_x[:,None,...])\n","\n","\n","\n","batch_size = test_data.size(0)\n","all_size = batch_size * 1\n","\n","stats = dict()\n","for i in range(4):\n","    stats[i] = 0\n","\n","# avgs = torch.zeros(iters, 2, 28*28)\n","\n","\n","\n","\n","# weight = 0.3\n","\n","\n","f, axarr = plt.subplots(4, 10, )\n","f.set_figheight(7)\n","f.set_figwidth(20)\n","\n","\n","iters = 500 # !!\n","    \n","\n","\n","# for idx, weight in enumerate([1, .8, .6, .4, .2, 0]):\n","for idx, weight in enumerate([.3, .2, 0.1, 0]):\n","\n","    p0_0 = [] \n","    p0_1 = [] \n","    p1_0 = [] \n","    p1_1 = [] \n","    p0_0_z = [] \n","    p0_1_z = [] \n","    p1_0_z = [] \n","    p1_1_z = [] \n","\n","\n","    for kk in range(iters):\n","            # print(kk)\n","            \n","            z_orig = torch.rand(all_size, 28, 28) #.cuda()  \n","            z_orig.cuda()\n","\n","            # z = (1*z + weight*test_data)/ (weight+1)  # noise + stim\n","\n","            z = (1-weight)*z_orig + weight*test_data #/ (weight+1)  # noise + stim\n","            z.cuda()\n","\n","            pred = model(z[:,None,...].cuda())\n","            \n","            pred = pred.squeeze()\n","            \n","          #   idx = (pred.squeeze()==0) & (test_targets==0)\n","            \n","          #   z[idx].mean(dim=0)\n","          #   import pdb; pdb.set_trace() \n","            idx0 = (pred==0) & (test_targets==0)   # it was 1 predicted 1\n","            p0_0.append(z[idx0].mean(dim=0))\n","            p0_0_z.append(z_orig[idx0].mean(dim=0))            \n","            \n","            idx1 = (pred==0) & (test_targets==1)   # it was 7 predicted 1\n","            p0_1.append(z[idx1].mean(dim=0))\n","            p0_1_z.append(z_orig[idx1].mean(dim=0))      \n","\n","            idx2 = (pred==1) & (test_targets==0)   # it was 1 predicted 7\n","            sum2 = z[idx2].mean(dim=0)\n","            if not torch.isnan(sum2).sum():\n","              p1_0.append(sum2)\n","              p1_0_z.append(z_orig[idx2].mean(dim=0))                    \n","\n","            idx3 = (pred==1) & (test_targets==1)   # it was 7 predicted 7\n","            sum3 = z[idx3].mean(dim=0)\n","            if not torch.isnan(sum3).sum():  \n","              p1_1.append(sum3)\n","              p1_1_z.append(z_orig[idx3].mean(dim=0))                    \n","            \n","            \n","            stats[0] += idx0.sum()\n","            stats[1] += idx1.sum()\n","            stats[2] += idx2.sum()\n","            stats[3] += idx3.sum()\n","\n","          #   for i in range(2):\n","          #     stats[i] += torch.sum(pred==i)\n","      \n","\n","    #   import pdb; pdb.set_trace()\n","    # for i in range(4):\n","    p0_0 = torch.stack(p0_0)#.squeeze()  \n","    a = torch.mean(p0_0, dim=0)\n","    axarr[idx, 0].imshow(a)    \n","    if not idx:\n","      axarr[idx, 0].set_title(f'predicted: {digit_p} - gt:{digit_p}') \n","    axarr[idx,0].axis('off')\n","   \n","\n","    # ------------  z  -----------------\n","    p0_0_z = torch.stack(p0_0_z)#.squeeze()  \n","    a_z = torch.mean(p0_0_z, dim=0)\n","    axarr[idx, 5].imshow(a_z)    \n","    if not idx:\n","      axarr[idx, 5].set_title(f'predicted: {digit_p} - gt:{digit_p}') \n","    axarr[idx,5].axis('off')\n","\n","\n","\n","    # plt.figure()\n","    # plt.imshow(a)\n","\n","    p0_1 = torch.stack(p0_1)#.squeeze()  \n","    b = torch.mean(p0_1, dim=0)\n","    axarr[idx, 1].imshow(b)    \n","    if not idx:    \n","      axarr[idx, 1].set_title(f'predicted: {digit_p} - gt:{digit_q}') \n","    axarr[idx,1].axis('off')\n","   \n","\n","\n","    # ------------  z  -----------------\n","    p0_1_z = torch.stack(p0_1_z)#.squeeze()  \n","    b_z = torch.mean(p0_1_z, dim=0)\n","    axarr[idx, 6].imshow(b_z)    \n","    if not idx:\n","      axarr[idx, 6].set_title(f'predicted: {digit_p} - gt:{digit_q}') \n","    axarr[idx,6].axis('off')\n","\n","\n","\n","    # plt.imshow(b)\n","\n","    p1_0 = torch.stack(p1_0)#.squeeze()  \n","    c = torch.mean(p1_0, dim=0)\n","    axarr[idx, 2].imshow(c)    \n","    if not idx:    \n","      axarr[idx, 2].set_title(f'predicted: {digit_q} - gt:{digit_p}')    \n","    axarr[idx,2].axis('off')\n","\n","    # plt.imshow(c)\n","\n","    # ------------  z  -----------------\n","    p1_0_z = torch.stack(p1_0_z)#.squeeze()  \n","    c_z = torch.mean(p1_0_z, dim=0)\n","    axarr[idx, 7].imshow(c_z)    \n","    if not idx:\n","      axarr[idx, 7].set_title(f'predicted: {digit_q} - gt:{digit_p}') \n","    axarr[idx,7].axis('off')\n","\n","\n","\n","\n","    p1_1 = torch.stack(p1_1)#.squeeze()  \n","    d = torch.mean(p1_1, dim=0)\n","    axarr[idx, 3].imshow(d)    \n","    if not idx:    \n","      axarr[idx, 3].set_title(f'predicted: {digit_q} - gt:{digit_q}')    \n","    axarr[idx,3].axis('off')\n","\n","    # ------------  z  -----------------\n","    p1_1_z = torch.stack(p1_1_z)#.squeeze()  \n","    d_z = torch.mean(p1_1_z, dim=0)\n","    axarr[idx, 8].imshow(d_z)    \n","    if not idx:\n","      axarr[idx, 8].set_title(f'predicted: {digit_q} - gt:{digit_q}') \n","    axarr[idx, 8].axis('off')\n","\n","    # plt.figure()\n","    # plt.imshow(d)\n","\n","\n","\n","    # classification_img = (a+b) - (c+d)\n","    # plt.figure()\n","    # plt.imshow(classification_img)\n","\n","    classification_img = (a+b)  - (c+d)   #  (p0_0 + p1_0) - (p0_1 + p1_1)  (predicted sevens - predicted ones)\n","    # plt.figure()\n","    # plt.imshow(classification_img)\n","    axarr[idx, 4].imshow(classification_img)\n","    if not idx:      \n","      axarr[idx, 4].set_title(f'mean: {digit_p} - {digit_q}')\n","    axarr[idx,4].axis('off')\n","\n","\n","    # ------------  z  -----------------\n","    classification_img_z = (a_z+b_z)  - (c_z+d_z)   #  (p0_0 + p1_0) - (p0_1 + p1_1)  (predicted sevens - predicted ones)\n","    axarr[idx, 9].imshow(classification_img_z)\n","    if not idx:\n","      axarr[idx, 9].set_title(f'predicted: {digit_p} - gt:{digit_q}') \n","    axarr[idx,9].axis('off')\n","\n","\n","\n","\n","\n","\n","    plt.tight_layout()\n","\n","\n","    f.subplots_adjust(hspace=0) #, wspace=0.0, right = 0.8)\n","    f.show()\n","\n","    # classification_img2 = (c+d) - (a+b)   #  (p0_0 + p1_0) - (p0_1 + p1_1)  (predicted sevens - predicted ones)\n","    # plt.figure()\n","    # plt.imshow(classification_img2)\n","\n","\n","\n","    print(stats)\n","\n","\n","\n","\n"],"execution_count":60,"outputs":[{"output_type":"stream","text":["tensor(892) tensor(958)\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1386: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n","  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"],"name":"stderr"},{"output_type":"stream","text":["{0: tensor(443952, device='cuda:0'), 1: tensor(178751, device='cuda:0'), 2: tensor(497, device='cuda:0'), 3: tensor(212355, device='cuda:0')}\n","{0: tensor(889165, device='cuda:0'), 1: tensor(568034, device='cuda:0'), 2: tensor(642, device='cuda:0'), 3: tensor(249755, device='cuda:0')}\n","{0: tensor(1334663, device='cuda:0'), 1: tensor(1038230, device='cuda:0'), 2: tensor(704, device='cuda:0'), 3: tensor(251585, device='cuda:0')}\n","{0: tensor(1780103, device='cuda:0'), 1: tensor(1516611, device='cuda:0'), 2: tensor(786, device='cuda:0'), 3: tensor(251671, device='cuda:0')}\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABZUAAAH2CAYAAAAI4mJNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvXmYXVd55vutM481l0pVGi1blm0s\nPIBtbIaYNDMhITSBEEICueTp5NI33HSSzpyQbpJObpOmO08mntu5IYEEyAgEAjgEwmBjAx6w5Umy\nZqlU83jmad8/6qC93/dIp6pkqWpLfn/P48e1tPfZZ+21vvWttXfV/m3neZ4JIYQQQgghhBBCCCGE\nEGshstkVEEIIIYQQQgghhBBCCHH5oJvKQgghhBBCCCGEEEIIIdaMbioLIYQQQgghhBBCCCGEWDO6\nqSyEEEIIIYQQQgghhBBizeimshBCCCGEEEIIIYQQQog1o5vKQgghhBBCCCGEEEIIIdbMFXdT2Tn3\nYefc+9s/v9Q59/QGfa/nnLtmI75LXBoUO+JCUeyIC0WxI4TYaJR3xIWguBEXimJHXCiKHXGhKHY2\njivupnIQz/O+5nnevtX2c8690zn39Y2oU/v7PuycqznnCoH/opfou97nnPtoWOpzuaDYWVvstPd7\nhXPuIedc0Tl3yjn3lktRn8uFsMZO+zs3pK/Wcm7t+KpTLO+5FPW5XFDsrP3cnHO3Oue+2o6bSefc\ney9FfcT6uNhzlHMu6px7v3Nu3Dm37Jx72DnXdzHr/FwnrHknbOsdrZURxY3WyRdKWGOn/Z2hWeto\nndyJYkfr5AslrLFzpcxZob6p7JyLbXYdLiH/j+d5ucB/TdXn4qHY2RicczeY2V+b2a+aWa+Z3WRm\nD25WfS4GV2rshLSvPkGxfGST6/OsUOxsWH2GzOzzZvYhMxs0s2vM7J7Nqo/o4GLOUb9lZneZ2Z1m\n1mNm7zCzysWo5JXClZp32oRmvRPS+lwwipuNIWzz58XgSo2dkPaV1smXAWGLnStxnXylxk6by37O\n2vCbys65Y865X3bOPeGcm3fO/blzLtXednf7bvgvOucmzOzP2//+fc65R5xzC865+5xzzw8c75b2\nnfRl59wnzCwV2Ha3c+5UoLzDOfcPzrlp59ysc+4PnXPXm9mfmtmd7d8MLLT3TTrnPuCcO9H+7c6f\nOufSgWP9gnPujFv565mfuNTt1g3n3I855463z+nX2238Cufca8zsV8zsre1z+85m1vPZoti5+FyE\n2Pk1M/uQ53mf8zyv4XnerOd5hzfuDNaGYsfMLnJfOede5Zx72jm36Jz7Y+fcV5xz7z7fuV2uKHbM\nLHyx85/M7Aue5/2V53lVz/OWPc978kLrcznQjsNfcM496lb+cuDPnHMjzrnPtWPpi865/sD+L2rH\n3oJz7jvOubsD297lnHuy/bkjzrn/ENj23Zj+OefcVDtm3rXBp/vduvSb2f9tZj/ped5xb4UDnudd\n8TeVlXcuPu45sFZW3Fx8LkLcaJ18+cRO2NY6lwWKHTMLX+xcFutkxc7Fx23WnOV53ob+Z2bHzOyA\nme0wswEzu9fM3t/edreZNczs98wsaWZpM7vFzKbM7A4zi5rZj7ePkTSzhJkdN7OfNbO4mb3ZzOp0\nvFPtn6Nm9h0z+6CZZW0lyF7S3vZOM/s61fODZvbpdh3zZvZPZvbf2tteY2aTZnZj+1h/bWaemV3T\n3v4jZvZolzb4sJnNtf970Mz+/bNozxvMrGBmL2m3xwfabfCK9vb3mdlH6TO/ZGafuRT1Uew852Ln\niJn9VzN7zMzOmNlHzWxgs2NFsXPONrhofWVmQ2a2ZGZvMrOYmb233Qbv7nJuUL92fC22Y/lxM/vp\nzY4Txc5lEztfMrP/ZWb3tdv6n8xs52bHygbE4f1mNmJm29rn/VA73lLtNvnN9r7bzGzWzF5nK388\n8Mp2ebi9/fVmdrWZOTP7HjMrmdmtFNP/pR2jr2tv719jrHzYLt4c9TIzWzCzXzSzCTM7aGbv2ey+\n2MD+fq7nnYsZS8+JtbLiJpRxo3Xy5RM7YVvrvM+0TlbsXMHrZMXOlTNnbVbw/FSg/DozOxzo7JqZ\npQLb/8TM/isd42lbuRB6mZmNm5kLbLvvPMFzp5lNm1nsHHWC4LGVC62imV0d+Lc7zexo++f/z8x+\nN7Dt2mDwrKENbrWVRxFi7fNfNrMXX2B7/oaZfSxQzrTb8LzBcynro9h5zsVOrd0v15pZzsz+3sz+\narNjRbFzafvKzH7MzL5BdT9pXRY85zjGDWY2ZisT+122MnG9bbNjRbFzWcTOQVu52XibrSwE/8DM\n7t3sWNmAOHx7oPz3ZvYngfL/ZWafbP/8i2b2Efr8F8zsx89z7E+a2XsDMVgOxp2tLOBftMZ6Xsw5\n6kfacfpntnIx8fz2mHjlZvfHBvX3cz3vhG29E/q1suImlHGjdfLlEzthW+tonazYudDYuSzWyYqd\nK2fO2iyn8snAz8dtJWF+l2kPH23cZWY/1/4T94X2n6HvaH9mzMxOe+0WCBzvXOwws+Oe5zXWUL9h\nW+mEBwPf+fn2v1v7e/kc1ozneQ95K39K3vA875/N7K9s5bdRHTjnHne+tPul59gF6uJ5XslW/iLp\nktQnBCh2QhQ7tnLz4c89zzvoeV7BzH7HVhJiGHlOx46to6/cyiP1342dt59jF44dz8xOnWO/8+J5\n3hOe5417ntf0PO8+W/mN+pvXc4wNRLETothp1+cfPc/7Vrvtf8vM7nLO9a7zOJcbk4Gfy+co59o/\n7zKzH6IYfImZjZqZOede65y73zk31972Olv5y5jvMktxVwocuysXeY4qt///XzzPK3ue96iZfdzC\nO8dcbJ7TeSds653LaK2suAlR3JjWycHjnYvQxI6FbK2jdTIc71wodrrX53JZJz+nY+dKmbM2S3i9\nI/DzTlv5rcJ38Wjfk2b2257n/TYfxDn3PWa2zTnnAgG008zO5f04aWY7nXOxcwQQf+eMrTTo8zzP\nO32OY505xzk8Gzxb+S1I5wbPe94qnz1jZmffZNn2uwzSsS9afUKAYqfz+zczdh6l/S4k3jaK53rs\nrLmvPM977SrHOmNm279bcM65YLnbsbugvNN5HMXOs6zPc5STtvKXyj/JG5xzSVv5q4MfM7NPeZ5X\nd8590i7d2Hs2c9SjgWPYOX6+0nmu5x1ms9c7a67PJqO46fx+rZPXxnM9dsK21un4WgtnzjFT7IQt\ndpR3/OOFPXaYy3LO2qy/VH6Pc267c27AVt4s+Iku+/6/ZvZTzrk73ApZ59zrnXN5M/uGrbhWfsY5\nF3fOvcnMbj/Pcb5pKw39u+1jpJxzL25vmzSz7c65hJmZ53mt9vd+0Dm3xczMObfNOffq9v5/Y2bv\ndM7d4JzLmNlvrufknXNvds7lnHMR59yrzOxHbcXTciH8nZm9wTl3V7v+7zMMxEkz2+2cO29fX+T6\nXGoUOyGKHVuR5r/LObenfT6/ZGafucD6XGqe07FjF7evPmtm+51zb3Qrb+N9j5ltDWyHczsXzrkf\ncM71t9v3djP7GTP71AXW51Kj2AlR7LTr84POuZudc3Ez+3VbeVRt8QLrdKXxUVvJ7a92zkXbsXO3\nc267rTjWkrby2F/DOfdaM3vVxfriizlHeSsvBvmamf2qW3lJyvVm9sMW3jnmYvOczjthW+9c5Ppc\nShQ3IYob0zr5sokdC9lax2mdrNh5bqyTn9Oxc8XMWd7muFN+2cyesBXXy1+YWcYj1wl95jVm9q32\n/mfM7G/NLN/e9kIze9hW/COfaP/X4U5pl3faijtw1lZ+6/AH7X9P2MoAnjOzmfa/pWzlz72P2Ios\n/Ukz+5nAsX7JVl4cM25mP2Eo5H67mT3epQ2+Zivi/SVbkYT/8LNs03ea2Yn2ef26mZ02s5e2tw2a\n2dfNbN7MHmr/26+Y2ecuVX0UO8+d2Gn/22/Zyg2KaTP7iLVf6BSm/xQ7F7+v2u1zsB2Pf2wrk/k7\nupwb1M/MPtZuk4KZPRU8zzD9p9gJX+y0/+2nbSVfzdvKCzN2bHasbEAcviJQ/qiZvS9QfreZfTFQ\nvsPMvtJux+l2m+5sb3uPrSwsF9p9+fHzxSB/92qxYhd/jtpmK48ZFtqx/R82uy82sL+f03nnEsTS\nO+0KXysrbsIXN+1/0zr5Moidi91XpnWyYmeTYqf9b6FfJyt2rpw5y7U/uGE4547Zimj8ixv6xc8R\nnHM5Wxlkez3PO7rZ9bmYKHYuLYodcaG0f+N5ylZeJPblza7PxUSxc2m5kmNHiAtFeefScqWudxQ3\nl5YrNW7MFDuXmit5raPYubQodsSFspFz1mbpL8RFxDn3BudcxjmXNbMPmNljtvKbHyG6otgRF4pb\nebS+z604Wn/FVh6vuX+TqyUuAxQ7QoiNRusdcSEobsSForWOuFAUO+JC2aw5SzeVrwx+wFb+3H7c\nzPbayp/Nb+yfoIvLFcWOuFDutJWXH8yY2RvM7I2e55U3t0riMkGxI4TYaLTeEReC4kZcKFrriAtF\nsSMulE2ZszZcfyGEEEIIIYQQQgghhBDi8kV/qSyEEEIIIYQQQgghhBBizeimshBCCCGEEEIIIYQQ\nQog1E9vIL3tl5IeenWvDufNvC7PGo1u9zdZfdz5et8+vtu8q2/+l9berVH5jeGX0LVgxR78P8VrW\ndfuF7nupWa0uz3b7er/v2Ryb+JfGx0MRO6++5TcgdlwDz6s2nIVyYrp4/oO18LOtXArKkVIN96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XOe3TWoeYyE5SzmKcnmkxJLEy5T1OpiDxGk6przksa+7F/1g5S3ndyibmVWG/P6r99EYID8X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9El9mzot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TPa7reXCeXnsH2s+ZxfBXxVqTDX3/5E+58ZuXxGlsJ3Je/FfPjp1+A+M+37oX4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\n","text/plain":["<Figure size 1440x504 with 40 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"wloEh56R76-s","colab_type":"code","outputId":"a77a32ec-5f8b-4359-8334-5c40a4c487b6","executionInfo":{"status":"ok","timestamp":1569266290593,"user_tz":240,"elapsed":409,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# p0_1\n","# z[idx1]\n","axarr.shape\n","jj"],"execution_count":57,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0"]},"metadata":{"tags":[]},"execution_count":57}]},{"cell_type":"code","metadata":{"id":"XLzeq3MY5FBQ","colab_type":"code","outputId":"0ffa0b02-2f84-48bb-8485-0705033b97b9","executionInfo":{"status":"ok","timestamp":1569255090484,"user_tz":240,"elapsed":907,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["digit_q"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["7"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"code","metadata":{"id":"2wiqn8naTyGH","colab_type":"code","outputId":"e76bd7c8-62f4-4777-95c5-7b715a1c5409","executionInfo":{"status":"ok","timestamp":1569255328257,"user_tz":240,"elapsed":9037,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":292}},"source":["# tSNE\n","\n","!pip install swat"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Collecting swat\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d0/07/da4f83d108a3bb58e99e55fe098a4c5c286e96dddf5f5424d7e3470bb78a/swat-1.5.2-cp36-cp36m-manylinux1_x86_64.whl (45.6MB)\n","\u001b[K     |████████████████████████████████| 45.6MB 1.9MB/s \n","\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from swat) (2.21.0)\n","Requirement already satisfied: pandas>=0.16.0 in /usr/local/lib/python3.6/dist-packages (from swat) (0.24.2)\n","Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from swat) (1.12.0)\n","Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->swat) (2.8)\n","Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->swat) (3.0.4)\n","Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->swat) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->swat) (2019.6.16)\n","Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.16.0->swat) (1.16.5)\n","Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.16.0->swat) (2018.9)\n","Requirement already satisfied: python-dateutil>=2.5.0 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.16.0->swat) (2.5.3)\n","Installing collected packages: swat\n","Successfully installed swat-1.5.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"zXQo48MKUmas","colab_type":"code","colab":{}},"source":["# import swat\n","# import seaborn as sns\n","\n","# # Create CAS Connection\n","# conn = swat.CAS(host, portnum, protocol='http')\n","# conn.sessionProp.setSessOpt(messageLevel='NONE'); # Suppress CAS Messages\n","\n","\n","# # Load CAS Action Sets\n","# conn.loadactionset('pca')\n","# conn.loadactionset('tsne');\n","\n","\n","# # Upload Digits and MNIST Data to CAS Server\n","# digits_cas = conn.upload_frame(digits, casout=dict(name='digits_df', replace=True))\n","# mnist_cas = conn.upload_frame(mnist, casout=dict(name='mnist_df', replace=True));\n","\n","# # Perform PCA: MNIST\n","# pca_mnist = conn.eig(table='mnist_df',\n","#                      n=30,\n","#                      output={'casOut':{'name':'pca_mnist','replace':'TRUE'},\n","#                              'copyVars':['ID','Label']})"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"XXgBCTPQWTGV","colab_type":"code","outputId":"5d5ccce8-f9eb-48f6-8db5-7c24d1acf9e9","executionInfo":{"status":"ok","timestamp":1569265254242,"user_tz":240,"elapsed":1938,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["\n","# save_path = 'drive/My Drive/classification_images/Binary_case/' + str(digit_p) + '_' + str(digit_q) + '/thresh_' + str(weight)\n","\n","# import os\n","# if not os.path.exists(save_path):\n","#     os.makedirs(save_path)\n","\n","\n","# # plt.title(f'gt: {str(kk)}  -  pred: {str(c.cpu().data[0].numpy())} - conf: {str(conf.cpu().data[0].numpy())} -  size-gt: {stats[kk]}')  \n","# # plt.imshow(a) #, cmap = 'gray')\n","# #fig.savefig(os.path.join(save_path, str(kk)+'-.png'))\n","\n","# # p0_0 = torch.stack(p0_0)#.squeeze()  \n","# a = torch.mean(p0_0, dim=0)\n","# fig = plt.figure()\n","# plt.imshow(a)\n","# plt.title(f'predicted: {digit_p} - gt:{digit_p}')\n","# fig.savefig(os.path.join(save_path, '1-1.png'))\n","\n","# # p0_1 = torch.stack(p0_1)#.squeeze()  \n","# b = torch.mean(p0_1, dim=0)\n","# fig = plt.figure()\n","# plt.imshow(b)\n","# plt.title(f'predicted: {digit_p} - gt:{digit_q}')\n","# fig.savefig(os.path.join(save_path, '1-7.png'))\n","\n","# # p1_0 = torch.stack(p1_0)#.squeeze()  \n","# c = torch.mean(p1_0, dim=0)\n","# fig = plt.figure()\n","# plt.imshow(c)\n","# plt.title(f'predicted: {digit_q} - gt:{digit_p}')\n","# fig.savefig(os.path.join(save_path, '7-1.png'))\n","\n","\n","# # p1_1 = torch.stack(p1_1)#.squeeze()  \n","# d = torch.mean(p1_1, dim=0)\n","# fig = plt.figure()\n","# plt.imshow(d)\n","# plt.title(f'predicted: {digit_q} - gt:{digit_q}')\n","# fig.savefig(os.path.join(save_path, '7-7.png'))\n","\n","\n","# # classification_img = (a+b) - (c+d)\n","# # plt.figure()\n","# # plt.imshow(classification_img)\n","\n","# classification_img = (a+b)  - (c+d)   #  (p0_0 + p1_0) - (p0_1 + p1_1)  (predicted sevens - predicted ones)\n","# fig = plt.figure()\n","# plt.imshow(classification_img)\n","# plt.title(f'mean: {digit_p} - {digit_q}')\n","# fig.savefig(os.path.join(save_path, 'classification_img.png'))\n","\n","\n","\n","# # a.shape\n","# # p0_0.shape\n","# # # z.shape\n","# # stats[0]\n","\n","\n","# # # classification_img = (b+d) - (a+c) \n","# # classification_img =   (c+d) - (a+b)\n","# # plt.figure()\n","# # plt.imshow(classification_img)\n"],"execution_count":46,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAEICAYAAACQ6CLfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmDstcaGJslsbG3M39dIjQLxEt/XlpxKnyv7/0OkFfnnnix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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"HnC0ZFmTnCcu","colab_type":"code","outputId":"e0c9cc32-4f51-43b4-af72-79d5222ae88d","executionInfo":{"status":"ok","timestamp":1569255209994,"user_tz":240,"elapsed":3612,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["#  SKIP THIS!!!!!\n","\n","# embed the signal in noise\n","\n","# - get a random test digit and add it to random noise\n","# - feed to the net and collect the answer\n","# - do record the averages\n","\n","# Load data\n","\n","# digit_p, digit_q =  1, 7\n","\n","\n","test = MNIST('./data', train=False, download=True , transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","data2 = test\n","# selecting number 0 zero only\n","tt = data2.targets[(data2.targets== digit_p) | (data2.targets== digit_q)]\n","tt[tt==digit_p] =  0\n","tt[tt==digit_q] = 1\n","dd = data2.data[(data2.targets== digit_p) | (data2.targets== digit_q)] \n","# tt = data.targets[(data.targets== 1)]\n","# dd = data.data[(data.targets== 1)] \n","\n","data2.targets = tt\n","data2.data = dd\n","test_loader = torch.utils.data.DataLoader(data2, batch_size=100, shuffle=True, drop_last = True)\n","# Num batches\n","# num_batches = len(train_loader)\n","print((tt==0).sum(), (tt==1).sum())\n","\n","# model.cuda()\n","model.eval()\n","test_data = test.data.type(torch.FloatTensor) # Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","test_data = test_data/256\n","\n","test_targets = test.targets.cuda()\n","#     output = model(evaluate_x[:,None,...])\n","\n","\n","\n","batch_size = test_data.size(0)\n","all_size = batch_size * 1\n","\n","stats = dict()\n","for i in range(4):\n","    stats[i] = 0\n","\n","iters = 100\n","    \n","# avgs = torch.zeros(iters, 2, 28*28)\n","\n","p0 = [] \n","p1 = [] \n","\n","\n","weight = 0.02\n","\n","for kk in range(iters):\n","  print(kk)\n","  \n","  z = torch.rand(all_size, 28, 28) #.cuda()  \n","  z.cuda()\n","\n","  z = (1*z + weight*test_data)/ (weight+1)  # noise + stim\n","  pred = model(z[:,None,...].cuda())\n","  \n","  pred = pred.squeeze()\n","  \n","#   idx = (pred.squeeze()==0) & (test_targets==0)\n","  \n","#   z[idx].mean(dim=0)\n","#   import pdb; pdb.set_trace() \n","  idx0 = (pred==0)   # predicted 1\n","  p0.append(z[idx0].mean(dim=0))\n","  \n","\n","  idx1 = (pred==1)   # it predicted 7\n","  p1.append(z[idx1].mean(dim=0))\n","\n","  \n","  stats[0] += idx0.sum()\n","  stats[1] += idx1.sum()\n","#   for i in range(2):\n","#     stats[i] += torch.sum(pred==i)\n","  \n","\n","#   import pdb; pdb.set_trace()\n","# for i in range(4):\n","p0 = torch.stack(p0)#.squeeze()  \n","a = torch.mean(p0, dim=0)\n","plt.figure()\n","plt.imshow(a)\n","\n","p1 = torch.stack(p1)#.squeeze()  \n","b = torch.mean(p1, dim=0)\n","plt.figure()\n","plt.imshow(b)\n","\n","\n","\n","# classification_img = (a+b) - (c+d)\n","# plt.figure()\n","# plt.imshow(classification_img)\n","\n","classification_img = (b) - (a)   #   (predicted sevens - predicted ones)\n","plt.figure()\n","plt.imshow(classification_img)\n","\n","print(stats)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["tensor(1135) tensor(1028)\n","0\n","1\n","2\n","3\n","4\n","5\n","6\n","7\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1386: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n","  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"],"name":"stderr"},{"output_type":"stream","text":["8\n","9\n","10\n","11\n","12\n","13\n","14\n","15\n","16\n","17\n","18\n","19\n","20\n","21\n","22\n","23\n","24\n","25\n","26\n","27\n","28\n","29\n","30\n","31\n","32\n","33\n","34\n","35\n","36\n","37\n","38\n","39\n","40\n","41\n","42\n","43\n","44\n","45\n","46\n","47\n","48\n","49\n","50\n","51\n","52\n","53\n","54\n","55\n","56\n","57\n","58\n","59\n","60\n","61\n","62\n","63\n","64\n","65\n","66\n","67\n","68\n","69\n","70\n","71\n","72\n","73\n","74\n","75\n","76\n","77\n","78\n","79\n","80\n","81\n","82\n","83\n","84\n","85\n","86\n","87\n","88\n","89\n","90\n","91\n","92\n","93\n","94\n","95\n","96\n","97\n","98\n","99\n","{0: tensor(16, device='cuda:0'), 1: tensor(215184, device='cuda:0'), 2: 0, 3: 0}\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:395: UserWarning: Warning: converting a masked element to nan.\n","  dv = (np.float64(self.norm.vmax) -\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:396: UserWarning: Warning: converting a masked element to nan.\n","  np.float64(self.norm.vmin))\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:403: UserWarning: Warning: converting a masked element to nan.\n","  a_min = np.float64(newmin)\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:408: UserWarning: Warning: converting a masked element to nan.\n","  a_max = np.float64(newmax)\n","/usr/local/lib/python3.6/dist-packages/matplotlib/colors.py:918: UserWarning: Warning: converting a masked element to nan.\n","  dtype = np.min_scalar_type(value)\n","/usr/local/lib/python3.6/dist-packages/numpy/ma/core.py:713: UserWarning: Warning: converting a masked element to nan.\n","  data = np.array(a, copy=False, subok=subok)\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure 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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"NA1u-WU5EORC","colab_type":"code","outputId":"57c21dbe-e38a-4de9-8a5c-917a7a4cc5c3","executionInfo":{"status":"error","timestamp":1565971932576,"user_tz":240,"elapsed":177,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":232}},"source":["# y_pred.max()\n","# z[k:k+batch_size][0].max()\n","y_pred.max()\n","pred.max()\n","\n","test.targets.max()\n","y_pred = model(stimulus.cuda())"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1386: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n","  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"],"name":"stderr"},{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-78-9dbeb0047452>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mconf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstimulus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 2)"]}]},{"cell_type":"code","metadata":{"id":"Ogisc8JiE6is","colab_type":"code","outputId":"3dd33dfa-db53-48c5-f7d8-30168c6a31c1","executionInfo":{"status":"ok","timestamp":1565971926203,"user_tz":240,"elapsed":124,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# y_pred[y_pred <= 0.5]= 0\n","y_pred.max()\n","# stimulus[0].max()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(1., device='cuda:0', grad_fn=<MaxBackward1>)"]},"metadata":{"tags":[]},"execution_count":77}]},{"cell_type":"code","metadata":{"id":"kQr1xsRUD-cJ","colab_type":"code","outputId":"ba58e575-5173-442e-cb60-3c672a44e9ee","executionInfo":{"status":"ok","timestamp":1565927551997,"user_tz":240,"elapsed":152,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["stats"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{0: tensor(54759), 1: tensor(377841)}"]},"metadata":{"tags":[]},"execution_count":160}]},{"cell_type":"code","metadata":{"id":"EaKe1LnTDYmE","colab_type":"code","outputId":"d7adf133-4dc2-4d03-edbd-955f5d5425c0","executionInfo":{"status":"ok","timestamp":1565928174103,"user_tz":240,"elapsed":284,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["plt.imshow(stimulus[4])\n","stimulus[0].max()\n","# stimulus"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(0.9565)"]},"metadata":{"tags":[]},"execution_count":172},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nT2UTamJM33bGWwoh3lfI/EJEiswvRK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rYHA51AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"CpN-0gUqDiWO","colab_type":"code","outputId":"5dd436ec-8cc8-4cb0-9c61-453dbf8a2d16","executionInfo":{"status":"ok","timestamp":1565999339085,"user_tz":240,"elapsed":1391,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["avgs.shape\n","weight\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.05"]},"metadata":{"tags":[]},"execution_count":260}]},{"cell_type":"code","metadata":{"id":"GviiULyj_ld-","colab_type":"code","outputId":"c75f7875-d3eb-4361-bba4-8265754ac9e5","executionInfo":{"status":"ok","timestamp":1565999683859,"user_tz":240,"elapsed":2731,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# stats\n","weight = 0\n","\n","for p in range(2):\n","#   pred.shape\n","  # z.shape\n","  z = torch.rand(all_size, 28, 28)\n","\n","  # plt.imshow(stimulus[1])\n","  plt.figure()\n","  kk = (1*z[p] + weight*test_data[p])/ (weight+1)\n","#   (test_data[110] + 5*z[p])/5\n","\n","  # plt.imshow(z[k:k+batch_size][0])\n","  plt.imshow(kk)\n","  # batch_size\n","  # z[k:k+batch_size][0].shape\n","  # z[k:k+batch_size][0].max()\n","  print(kk.min(), kk.max())\n","  print(z[p].min(), z[p].max())\n","  print(test_data[p].min(), test_data[p].max())\n","\n","  plt.figure()\n","  plt.imshow(test_data[p])\n","  \n","  plt.figure()\n","  plt.imshow(z[p])\n","  "],"execution_count":0,"outputs":[{"output_type":"stream","text":["tensor(8.2135e-05) tensor(0.9993)\n","tensor(8.2135e-05) tensor(0.9993)\n","tensor(0.) tensor(0.9961)\n","tensor(0.0032) tensor(1.0000)\n","tensor(0.0032) tensor(1.0000)\n","tensor(0.) tensor(0.9961)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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size 432x288 with 1 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0fngVgYOTngQBej+Na/oXKMrnZmiyNOB+7SjfxWlVj/gWgB0o/8V8P4PfxWIOxruYA\nlkW+VsZ7bQCmofRl4FGUfjZyE4A6AOYDWAdgHoDalWhtL6B0mvNylBotM05r64rSl/TLASyNfPWI\n97Fz1hWX48YdfoQECj/wIyRQaH5CAoXmJyRQaH5CAoXmJyRQaH5CAoXmJyRQaH5CAuX/ASZZoAeE\nC0seAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"rbPFYc9sXzCC","colab_type":"code","outputId":"dfac4373-6623-4ccb-e474-4a305e531712","executionInfo":{"status":"ok","timestamp":1565918462016,"user_tz":240,"elapsed":147,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# a.sum()\n","test_data = test.data.type(torch.FloatTensor) # Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","test_data.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([2167, 28, 28])"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"code","metadata":{"id":"ptMBZ_CwA39m","colab_type":"code","outputId":"a2a3043c-7b76-4eba-caf0-d1b672649e7d","executionInfo":{"status":"ok","timestamp":1565992380145,"user_tz":240,"elapsed":1019,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":521}},"source":["# plot the avg\n","dd = torch.mean(avgs, dim=0)\n","#dd = dd - grand_mean\n","for kk in range(2):\n","  \n","  fig = plt.figure()\n","  a = dd[kk]\n","  a = a.view(-1,28)\n","#   b = model(a[None,None,...].cuda())\n","\n","  #a = torch.nn.functional.log_softmax(a)# torch.nn.functional.softmax(a)\n","#   c = b.data.max(1)[1]\n","#   plt.title(f'gt: {str(kk)}  -  pred: {str(c.cpu().data[0].numpy())} -  size-gt: {stats[kk]}')  \n","  plt.imshow(a, cmap = 'gray')\n","#   fig.savefig(os.path.join(save_path, str(kk)+'-mlp.png'))"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wNE9RxTzzGQcgAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKflFklLyiySl5BdJ6v8BODYpYhpBX6gAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"GhF87Oj7XTas","colab_type":"text"},"source":["**End of the signal in NOISE analysis**"]},{"cell_type":"code","metadata":{"id":"kCTxYRTR6KNS","colab_type":"code","outputId":"24ff71af-dd1d-471b-e134-a159280fcaa3","executionInfo":{"status":"error","timestamp":1565823455892,"user_tz":240,"elapsed":6646,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":905}},"source":["EPOCHS = 30\n","losses = []\n","\n","model.train()\n","for epoch in range(EPOCHS):\n","    for batch_idx, (data, target) in enumerate(train_loader):\n","        # Get Samples\n","        data, target = Variable(data), Variable(target)\n","        \n","        if cuda:\n","            data, target = data.cuda(), target.cuda()\n","        \n","        # Init\n","        optimizer.zero_grad()\n","\n","        # Predict\n","        y_pred = model(data) \n","\n","        # Calculate loss\n","        loss = F.cross_entropy(y_pred, target)\n","        losses.append(loss.cpu().data)\n","#         losses.append(loss.cpu().data[0])        \n","        # Backpropagation\n","        loss.backward()\n","        optimizer.step()\n","        \n","        \n","        # Display\n","        if batch_idx % 100 == 1:\n","            print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n","                epoch+1,\n","                EPOCHS,\n","                batch_idx * len(data), \n","                len(train_loader.dataset),\n","                100. * batch_idx / len(train_loader), \n","                loss.cpu().data), \n","                end='')\n","    # Eval\n","    evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","    evaluate_y = Variable(test_loader.dataset.test_labels)\n","    if cuda:\n","        evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","    model.eval()\n","    output = model(evaluate_x[:,None,...])\n","    pred = output.data.max(1)[1]\n","    d = pred.eq(evaluate_y.data).cpu()\n","    accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","    \n","    print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\\t Test Accuracy: {:.4f}%'.format(\n","        epoch+1,\n","        EPOCHS,\n","        len(train_loader.dataset), \n","        len(train_loader.dataset),\n","        100. * batch_idx / len(train_loader), \n","        loss.cpu().data,\n","        accuracy*100,\n","        end=''))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n","  # This is added back by InteractiveShellApp.init_path()\n"],"name":"stderr"},{"output_type":"stream","text":["\r Train Epoch: 1/30 [256/60000 (0%)]\tLoss: 2.279951"],"name":"stdout"},{"output_type":"stream","text":["Exception in thread Thread-8:\n","Traceback (most recent call last):\n","  File \"/usr/lib/python3.6/threading.py\", line 916, in _bootstrap_inner\n","    self.run()\n","  File \"/usr/lib/python3.6/threading.py\", line 864, in run\n","    self._target(*self._args, **self._kwargs)\n","  File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/pin_memory.py\", line 21, in _pin_memory_loop\n","    r = in_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)\n","  File \"/usr/lib/python3.6/multiprocessing/queues.py\", line 113, in get\n","    return _ForkingPickler.loads(res)\n","  File \"/usr/local/lib/python3.6/dist-packages/torch/multiprocessing/reductions.py\", line 276, in rebuild_storage_fd\n","    fd = df.detach()\n","  File \"/usr/lib/python3.6/multiprocessing/resource_sharer.py\", line 57, in detach\n","    with _resource_sharer.get_connection(self._id) as conn:\n","  File \"/usr/lib/python3.6/multiprocessing/resource_sharer.py\", line 87, in get_connection\n","    c = Client(address, authkey=process.current_process().authkey)\n","  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 493, in Client\n","    answer_challenge(c, authkey)\n","  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 737, in answer_challenge\n","    response = connection.recv_bytes(256)        # reject large message\n","  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n","    buf = self._recv_bytes(maxlength)\n","  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n","    buf = self._recv(4)\n","  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n","    chunk = read(handle, remaining)\n","ConnectionResetError: [Errno 104] Connection reset by peer\n","\n"],"name":"stderr"},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-4-44792c2dbb8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEPOCHS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m         \u001b[0;31m# Get Samples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    574\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshutdown\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatches_outstanding\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 576\u001b[0;31m             \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    577\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatches_outstanding\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    578\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcvd_idx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_get_batch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    541\u001b[0m         \u001b[0;32melif\u001b[0m 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  \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_try_get_batch\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0;31m#   (bool: whether successfully get data, any: data if successful else None)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 511\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    512\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.6/queue.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    171\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mremaining\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_empty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m             \u001b[0mitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    297\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 299\u001b[0;31m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    300\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","metadata":{"id":"Qqb_-uW0WbhG","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Hf8VOEzhrdHr","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"35n9_KqYfGX5","colab_type":"code","outputId":"8270f6b0-13e2-48a9-da23-b522f3b121d3","executionInfo":{"status":"ok","timestamp":1564872352509,"user_tz":240,"elapsed":313,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":71}},"source":["save_path = 'drive/My Drive/classification_images'\n","torch.save(model,os.path.join(save_path, 'mlp.pth'))\n","torch.save(model.state_dict(), os.path.join(save_path, 'mlp_state.pth'))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torch/serialization.py:256: UserWarning: Couldn't retrieve source code for container of type Model. It won't be checked for correctness upon loading.\n","  \"type \" + obj.__name__ + \". It won't be checked \"\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"7ind_L9ifSmr","colab_type":"code","outputId":"2caf0861-09ab-4f68-da10-0e92bb26cbc0","executionInfo":{"status":"ok","timestamp":1564869515055,"user_tz":240,"elapsed":211,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["save_path"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'drive/My Drive/classification_images'"]},"metadata":{"tags":[]},"execution_count":154}]},{"cell_type":"code","metadata":{"id":"uhz_O9SP6N2C","colab_type":"code","outputId":"2adbbd8f-fa04-4a0f-9ecf-32333ec29608","executionInfo":{"status":"ok","timestamp":1564872961010,"user_tz":240,"elapsed":240,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":156}},"source":["evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","evaluate_y = Variable(test_loader.dataset.test_labels)\n","if cuda:\n","    evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","model.eval()\n","output = model(evaluate_x[:,None,...])\n","pred = output.data.max(1)[1]\n","d = pred.eq(evaluate_y.data).cpu()\n","accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","\n","print('Accuracy:', accuracy*100)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Accuracy: tensor(98.3000, dtype=torch.float64)\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: test_data has been renamed data\n","  warnings.warn(\"test_data has been renamed data\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: test_labels has been renamed targets\n","  warnings.warn(\"test_labels has been renamed targets\")\n","/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n","  # This is added back by InteractiveShellApp.init_path()\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"71RYIxeWrjKI","colab_type":"code","outputId":"c495f53d-e104-46fa-cafd-7fe9e2942b11","executionInfo":{"status":"ok","timestamp":1564873019766,"user_tz":240,"elapsed":320,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["import sklearn\n","from sklearn import metrics\n","sklearn.metrics.confusion_matrix(pred.cpu().numpy(), evaluate_y.cpu().numpy())\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 971,    0,    1,    0,    0,    1,    2,    1,    1,    2],\n","       [   1, 1125,    0,    0,    1,    0,    2,    3,    0,    2],\n","       [   0,    3, 1014,    3,    3,    0,    1,    7,    3,    1],\n","       [   1,    1,    1,  994,    1,    2,    1,    2,    4,    3],\n","       [   0,    0,    1,    0,  959,    0,    1,    0,    1,    5],\n","       [   1,    1,    0,    4,    1,  874,    6,    0,    0,    2],\n","       [   2,    2,    2,    0,    4,    2,  940,    0,    1,    1],\n","       [   1,    1,    6,    2,    3,    2,    0, 1006,    3,    2],\n","       [   2,    2,    6,    3,    2,    9,    5,    5,  961,    5],\n","       [   1,    0,    1,    4,    8,    2,    0,    4,    0,  986]])"]},"metadata":{"tags":[]},"execution_count":55}]},{"cell_type":"code","metadata":{"id":"FTXQe7ko7ZQV","colab_type":"code","outputId":"a862a066-1a01-45d5-dda7-4cfaa4dddfb9","executionInfo":{"status":"ok","timestamp":1564872466045,"user_tz":240,"elapsed":101239,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["batch_size = 10000\n","all_size = 100000\n","\n","stats = dict()\n","for i in range(10):\n","    stats[i] = 0\n","  \n","    \n","avgs = torch.zeros(100, 10, 28*28)\n","\n","for kk in range(100):\n","  print(kk)\n","  \n","#   z = torch.rand(all_size, 1, 28, 28)*2 -1 #.cuda()\n","  z = torch.rand(all_size, 1, 28, 28) #.cuda()\n","  \n","  z.cuda()\n","  #plt.imshow(z[1].reshape(28,28))\n","\n","\n","  all_preds = []\n","  all_idx = torch.ones(all_size, dtype = torch.uint8)\n","  for k in range(0,all_size, batch_size):\n","      y_pred = model(z[k:k+batch_size].cuda())\n","      y_pred[y_pred <= 0]= 0\n","\n","      indices = torch.ones(y_pred.size(0), dtype = torch.uint8)\n","      indices[torch.mean(y_pred, dim =1)==0] = 0 \n","\n","      all_idx[k:k+batch_size] = indices \n","      pred = y_pred[indices==1].data.max(1)[1]\n","\n","      all_preds.append(pred)\n","\n","  pred = torch.cat(all_preds)\n","\n","  for i in range(10):\n","    stats[i] += torch.sum(pred==i)\n","  \n","  z = z[all_idx]\n","  for i in range(10):\n","      a = torch.mean(z[pred==i] , dim=0) \n","      avgs[kk, i] = a.reshape(28*28)\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n","  # This is added back by InteractiveShellApp.init_path()\n"],"name":"stderr"},{"output_type":"stream","text":["1\n","2\n","3\n","4\n","5\n","6\n","7\n","8\n","9\n","10\n","11\n","12\n","13\n","14\n","15\n","16\n","17\n","18\n","19\n","20\n","21\n","22\n","23\n","24\n","25\n","26\n","27\n","28\n","29\n","30\n","31\n","32\n","33\n","34\n","35\n","36\n","37\n","38\n","39\n","40\n","41\n","42\n","43\n","44\n","45\n","46\n","47\n","48\n","49\n","50\n","51\n","52\n","53\n","54\n","55\n","56\n","57\n","58\n","59\n","60\n","61\n","62\n","63\n","64\n","65\n","66\n","67\n","68\n","69\n","70\n","71\n","72\n","73\n","74\n","75\n","76\n","77\n","78\n","79\n","80\n","81\n","82\n","83\n","84\n","85\n","86\n","87\n","88\n","89\n","90\n","91\n","92\n","93\n","94\n","95\n","96\n","97\n","98\n","99\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"J5Z4VfN0QpBL","colab_type":"code","colab":{}},"source":["z = torch.rand(1000000, 784)\n","grand_mean = torch.mean(z,dim=0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"tNu7VTILZxE1","colab_type":"code","outputId":"ddf771bd-35ac-4d42-bac2-755a5ea60884","executionInfo":{"status":"ok","timestamp":1564871400043,"user_tz":240,"elapsed":293,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["(indices==1).sum()\n","y_pred.size(0)\n","y_pred[1]"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], device='cuda:0',\n","       grad_fn=<SelectBackward>)"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"id":"P_qVlUIwZwf9","colab_type":"code","outputId":"54aee26d-d87c-4604-9d94-bb6c2e85f1d9","executionInfo":{"status":"ok","timestamp":1564872471624,"user_tz":240,"elapsed":214,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["stats"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{0: tensor(27958, device='cuda:0'),\n"," 1: tensor(0, device='cuda:0'),\n"," 2: tensor(5183612, device='cuda:0'),\n"," 3: tensor(2409211, device='cuda:0'),\n"," 4: tensor(0, device='cuda:0'),\n"," 5: tensor(2240440, device='cuda:0'),\n"," 6: tensor(51097, device='cuda:0'),\n"," 7: tensor(81470, device='cuda:0'),\n"," 8: tensor(6207, device='cuda:0'),\n"," 9: tensor(5, device='cuda:0')}"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"code","metadata":{"id":"dvHCYz6xRALW","colab_type":"code","outputId":"5822fbd3-1267-4a02-b69d-e258154d41ab","executionInfo":{"status":"ok","timestamp":1564865785774,"user_tz":240,"elapsed":213,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["grand_mean.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([784])"]},"metadata":{"tags":[]},"execution_count":69}]},{"cell_type":"code","metadata":{"id":"PLTo_R17UUvZ","colab_type":"code","outputId":"3cdd81db-4288-4105-dae1-d6e08607f939","executionInfo":{"status":"ok","timestamp":1564866669275,"user_tz":240,"elapsed":305,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["b.data.max(1)[1]\n","b\n","a.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([28, 28])"]},"metadata":{"tags":[]},"execution_count":77}]},{"cell_type":"code","metadata":{"id":"WaJqeovzgriQ","colab_type":"code","colab":{}},"source":["for i in range(10):\n","    print(torch.sum(pred==i))  \n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"A6DDnDB784s3","colab_type":"code","outputId":"8e7400b7-20b8-4287-8e3a-2d5145f6eb45","executionInfo":{"status":"ok","timestamp":1564872479682,"user_tz":240,"elapsed":2175,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["dd = torch.mean(avgs, dim=0)\n","#dd = dd - grand_mean\n","for kk in range(10):\n","  \n","  fig = plt.figure()\n","  a = dd[kk]\n","  a = a.view(-1,28)\n","  b = model(a[None,None,...].cuda())\n","\n","  #a = torch.nn.functional.log_softmax(a)# torch.nn.functional.softmax(a)\n","  c = b.data.max(1)[1]\n","  plt.title(f'gt: {str(kk)}  -  pred: {str(c.cpu().data[0].numpy())} -  size-gt: {stats[kk]}')  \n","  plt.imshow(a, cmap = 'gray')\n","  fig.savefig(os.path.join(save_path, str(kk)+'-mlp.png'))\n","  "],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n","  # This is added back by InteractiveShellApp.init_path()\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:395: UserWarning: Warning: converting a masked element to nan.\n","  dv = (np.float64(self.norm.vmax) -\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:396: UserWarning: Warning: converting a masked element to nan.\n","  np.float64(self.norm.vmin))\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:403: UserWarning: Warning: converting a masked element to nan.\n","  a_min = np.float64(newmin)\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:408: UserWarning: Warning: converting a masked element to nan.\n","  a_max = np.float64(newmax)\n","/usr/local/lib/python3.6/dist-packages/matplotlib/colors.py:918: UserWarning: Warning: converting a masked element to nan.\n","  dtype = np.min_scalar_type(value)\n","/usr/local/lib/python3.6/dist-packages/numpy/ma/core.py:713: UserWarning: Warning: converting a masked element to nan.\n","  data = np.array(a, copy=False, subok=subok)\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAEICAYAAACQ6CLfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nz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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAEICAYAAACQ6CLfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlMNvlpTDb5aUw2+W1P8B0Gs/4TQUPdMAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"REXMt5SvgdbK","colab_type":"code","outputId":"cf279773-9d32-4b57-dc0a-3c8f0d207efc","executionInfo":{"status":"ok","timestamp":1564869780908,"user_tz":240,"elapsed":313,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["for i in range(10):\n","    print(torch.sum(pred==i))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["tensor(236, device='cuda:0')\n","tensor(3, device='cuda:0')\n","tensor(1874, device='cuda:0')\n","tensor(160, device='cuda:0')\n","tensor(2689, device='cuda:0')\n","tensor(22, device='cuda:0')\n","tensor(44, device='cuda:0')\n","tensor(81, device='cuda:0')\n","tensor(93796, device='cuda:0')\n","tensor(98, device='cuda:0')\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"DQa2gDav9kKr","colab_type":"code","outputId":"db3940f6-bccd-4b68-e5d1-990a3e0776e4","executionInfo":{"status":"error","timestamp":1564867442930,"user_tz":240,"elapsed":288,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":300}},"source":["# ls\n","from google.colab import files\n","files.download('./*.png')\n"],"execution_count":0,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-96-9eb9ebc38cb1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./*.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mdownload\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m    142\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 144\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=undefined-variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m   \u001b[0mstarted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_threading\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEvent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: Cannot find file: ./*.png"]}]},{"cell_type":"code","metadata":{"id":"SBxJ8w3AYAbz","colab_type":"code","colab":{}},"source":["ls drive/My\\ Drive/classification_images\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"3GAnOj4gXjd8","colab_type":"code","outputId":"8b431de1-784f-4821-caca-18c31191ec3f","executionInfo":{"status":"ok","timestamp":1564867529154,"user_tz":240,"elapsed":24357,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":["from google.colab import drive\n","drive.mount('/content/drive/')"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"x1TYqjT17ejE","colab_type":"code","outputId":"ce93940e-d31e-48b7-cdee-26778485fc75","executionInfo":{"status":"ok","timestamp":1564860495533,"user_tz":240,"elapsed":2235,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# z = z[all_idx]\n","pred = torch.cat(all_preds)\n","for i in range(10):\n","    plt.figure()\n","    a = torch.mean(z[pred==i] , dim=0) \n","    a = a.view(-1,28)\n","    b = model(a[None,None,...].cuda())\n","    c = b.data.max(1)[1]\n","    plt.title(f'gt: {str(i)}  -  pred: {str(c.cpu().data[0].numpy())} -  size-gt: {z[pred==i].size(0)}')\n","    plt.imshow(1-a)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAEICAYAAACQ6CLfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJztnXt4VOW59u8n55AECInkwPnkARBB\nEVDEgvWsLWJbqnV7afUr7a5u2+rXfn7d7VW/ax/qtlut2t3ujUrV1rZbW92iVSuiApYNEpGjHMUA\nCSEcEiAJIYeZ5/tjVtxDzPvMEJKZkff+Xddcmcy93rXetWbdM2vW8z7PK6oKQoh/pCW7A4SQ5EDz\nE+IpND8hnkLzE+IpND8hnkLzE+IpNL9niIiKyOhk98NCRDaKyMxk9+NUxyvzi8hMEak6wTYiIv8i\nIgeDx7+IiPRWH1MJEckWkQUickRE9orI3YnYrqqOU9V3ErGtDkSkUkQuPYHls0Tkj0E7dX1YBctt\nOtHzLhF4Zf5uMg/AdQDOATABwBcAfDOpPQoQkfRe3sR9AMYAGAZgFoAfiMiVvbzNzxLvAvgbAHuN\nZb4PYH9iunOCqOop9QBwLoAPADQAeB7AfwL4RwB5AJoBhAE0Bo/yONa3HMC8qP9vB7Cil/o+E0AV\ngB8COACgEsBNUfpTAH4F4FUATQAuBZAN4F8B7AJQC+DfAeRGtfk+gBoAewDcBkABjI6zP3sAXB71\n/z8A+EMP7WsxgFcAHAJQB2AZgLRAqwRwafD8UNT71RT0f3igXQtgTbDMcgATjO3lAngaQD2ATQB+\nAKAq0H4TnBfNwXZ+cIL7UgVgZhevjwi2dVXHtlLpkfQO9OjOAFkAdgL4DoBMANcDaAXwj4E+s/Ob\nAOAiAIeMdR4GMDXq/8kAGnqp/zMBtAN4KDD154IT/oxAfyroz3RErtpyADwMYCGAAQAKALwM4KfB\n8lcGHwjjEfnw+120+QF8DcA6R18Kg2VLol77MoD1PbSvPw0+qDKDxwwAEmifmL9Tm38GsDRYfhKA\nfQCmAkgHcEvQLtuxvfsBLAn2azCAddHnQlfbDJb5Whz74jL/KwDmdHXepcLjVLvsnwYgA8Cjqtqm\nqi8AeM9qoKrvqmp/Y5F8RAzXwWEA+b38u//HqtqiqksA/BnA3CjtJVX9q6qGAbQg8rPke6pap6oN\niBjkhmDZuQB+raobVLUJkcv4T1DV36nqBEcf8oO/nfe94GR2LIo2AGUAhgXv1TINHNMVIvJVRD6s\nvqSqbYjs93+o6kpVDanq04gcj2mOVcwF8M+qWq+qVQAejdVBVZ2gqr87wf3q6O8cAOmq+mJ32ieC\nU8385QCqO51Eu09ynY0A+kb93xdAo3WidhDctW4MHjPi3F59YNQOdiKyXx1E789pAPoAeF9EDonI\nIQCvB68jaBe9/M44+wBE9hv49L43dLVwN/b1ZwC2A3hDRHaIyL2uBUVkEoBfAJijqh2/n4cBuKdj\nv4N9HwKgXERuiurLa8HynY/FyZ4XTkQkD8ADAO7qrW30BBnJ7kAPUwNgkIhIlDmHAPgoeN6dFMaN\niNzs67iCOCd4LSaqOq4b2ysUkbyoD4ChADZErzbq+QFEfqeOU9XqLtZVg8j+dzA03k6oar2I1CCy\nv4uCl537fqL7Glyl3IOIgccDeEtEVqnq4ujlRGQggP8CcIeqfhAl7QbwT6r6T45NPNvp/xpELvc/\nDP4f0knvyfTWMQCGA1gWXCBmAegnInsBTFPVyh7cVrc51b75/xtACMCdIpIhIrMBTInSawEUiUi/\nE1jnMwDuFpFBIlKOyAn7VE912MH/C0JEMxC5qfV8VwsFl/6PA3g4MAmCfl4RLPIcgFtFZKyI9AHw\nkxPsxzMAfiQihSJyJoBvoIf2XUSuFZHRwc+nw4i8b+FOy2QA+COA36rqc51W8TiAb4nI1CAcmyci\n14iI62fJcwD+b7AvgwDc2UmvBTDyBPchW0Rygn+zRCQn2J8NiHy4TAwe/ytY/0T04hXHCZPsmw49\n/UDkhtwaRC5bnwfwAiK/oTv0BQAOInKHuByRG02NxvoEkUu4uuDxAIIbU73Q95mI3Dz6e0S+1XcB\nuDlKfwrBzcuo13IQ+Z2/A8ARRO4u3xWl34tIKOpTd/sB3ARgo9Gf7OB4HUHk5L27B/f1e4jcZGsK\n9jn6PapEJJIxPOhvE/7njn8jgKHBclcCWBW8lzXB+13g2F4eInf1DwXH6EcAPorSZwfH+xCA/x28\nthFR0ZYu1lkZ9C/6Mdz1vibbG50fHXdXT1lEZCWAf1fVXye7L7EIBor8VlUHJ7svpzoi8rcAblDV\nzyW7L8niVLvsh4h8TkRKg8v+WxAZmPN6svtFkouIlInIdBFJE5EzEPn5lrJ34hPBqXbDDwDOQOT3\nXR4il8JfVtWa5HaJpABZAP4DkYE3hwD8AcAvk9qjJHPKX/YTQrrmlLvsJ4TER0Iv+9Pz8jSzcIBT\nL+h71Gzf0JLj1NIa7M+xUJ59hZOd1WbqbSF3Dk04ZG87IzNk6uEG+20oLjpi6vsa3YPusrPt/Wpp\nzjL144NvnyY9t91ufrT7p5jkxjhurTHymoy+Dy6sM5vub8039dYG+7il2V1HKNd9Pkp79wePttXX\nIdTUFNcKTsr8QYbXI4iMrX5CVe+3ls8sHIDBd37Pqc+6dI25vSWV7jT0vMX2m1V/Qaupnz7USswC\nqg+7hwY0Hc4125aUHDL1hndKTP22m+37lY+tuMSpnTHSvt2xdaMdWEg/an+w9R170NSPvl/s1MLp\n9gdyxlj7Q+/YbnukcVqL2wP3z+k8Buh45u++2NR3LbHHS2XXmzIOn+3+UM48GMOWxmGr+sXDdtso\nun3ZH6ST/hsiGUtjAdwoImO7uz5CSGI5md/8UwBsV9UdqtqKyN3T2T3TLUJIb3My5h+E44cqVgWv\nHYeIzBORChGpCDU1dZYJIUmi1+/2q+p8VZ2sqpPT8/J6e3OEkDg5GfNX4/jMqMHBa4SQzwAnY/5V\nAMaIyAgRyUKkgMTCnukWIaS36XaoT1XbReROAH9BJNS3QFXNPPe8gmZMm+VeJDvNjhlPH/qxU1tR\n5CpIE+Ha8etM/bWtdjq67nGPMcgd0WV9i0+o/cgd7gKAnExTxmPv2kVlC0rd29+yo8xeeYxwm8SI\n8x/ZVGS3z3avf+KMrWbbD5afbur5e+xw9pGx7nDawZAdGt66yw6/njbVrsl5oNI9ngWww3ntBfZB\nzy1rdGqSE2OAQRQnFedX1VcRKSZJCPmMweG9hHgKzU+Ip9D8hHgKzU+Ip9D8hHgKzU+IpyQ0n7+x\nMRfLl7sT/3JixMvL+rlTPM/7wganBgCvLZ5s6hkj3bFTAMgZ407LLc63cxY+asw29bSqGHnpMWLx\nR7e5JxzKHGzXSJhwhj0o8/2NdjXrGedsNvU1tZ9K9/iEVTHWnb/fjuNf//V3TD0nzR3nf3TTLLNt\nRq2dr1+/3x67kT642dRhpIEXj7BrDdSvN9Kkj8U/dyu/+QnxFJqfEE+h+QnxFJqfEE+h+QnxFJqf\nEE9JaKgvrR3I2e/+vJFae/Lc7cPdlYA+anOHlABg8ES7Ou+edaWmfvUlK53aW1V26mn/QjsUmDHD\nDnGmvXWaqeddWuvUJhTtMdsueW2Sqd963Tum/tTyi0z9kkkfOrWV4WFm21CRHep7+t0Zpp5W2OLW\ndtkVlzXTDq+GY+jlA+zKw3uNFPE0sdc9/Pwqp7Y/z65Sfdx24l6SEHJKQfMT4ik0PyGeQvMT4ik0\nPyGeQvMT4ik0PyGektA4P/JCkKnu1NiiPDv9tHWVuwx1+9BjZtuq2kJTzxpmp/S+8upUp3bOLLsE\n9ar1o0w9VsruNTe8b+qT8nc6tZ+uvspsO+0ydxweALY02iWsy0ccMPW333eXRF/xxYfMtjNXftPU\nB46wZwiub+jj1Aon2KW3m1rslN4+L9hjUvYW2fp3rnrNqT22bqbZtrTcPS4k1hiB45aNe0lCyCkF\nzU+Ip9D8hHgKzU+Ip9D8hHgKzU+Ip9D8hHhKQuP84bCg+ai7jPXBv7pLUANAqMw9dbGG7dzvnK3u\n/GkACPWxy2u39XXHT88qsGsFVOTZeeu/vPBZU7/7qdtNfcPF7vEPabvs/V4pw01da+z2WfX290fh\nVPc4gAuX3WG2PXfoblNfW23XcAiF3H0Lhe1+W+cpALTaJRwg1fZxe7T6aqd2xzXuMQAA8Miyy51a\nc7M9PiGakzK/iFQCaAAQAtCuqnZxfEJIytAT3/yzVNUe5kUISTn4m58QTzlZ8yuAN0TkfRGZ19UC\nIjJPRCpEpCLUYNeyI4QkjpO97L9IVatFZCCARSKyWVWXRi+gqvMBzAeA7JGD4s86IIT0Kif1za+q\n1cHffQBeBDClJzpFCOl9um1+EckTkYKO5wAuB2BPlUsISRlO5rK/BMCLItKxnt+p6uvmxjLCOM2o\nZ35shj2tca4Rm81MD5lt6wfZu5rRYE9tPPhMd2383663L3hmnW7n+9/1x9tM/dLZq039ja1nObXQ\ngHazbdYOu3791M9vNPXVL4039aPH3PHykhi17SvWjjb1omH1pt7+qnsq62OX2fXtQ832+RAe6p4T\nAAAyqu1xAiOmuscwPLboSrNtVpm77oVkxP/LutvmV9UdAM7pbntCSHJhqI8QT6H5CfEUmp8QT6H5\nCfEUmp8QT0loSq+q4FhrplM/0mCHnXLXu/U2O9IHjLZDXrE4+I47bVYG2OGVt9vP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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"vs1QSHq79WhR","colab_type":"code","outputId":"722456f8-7a9a-4498-b642-59035515fbf4","executionInfo":{"status":"error","timestamp":1564864922080,"user_tz":240,"elapsed":3211,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["for i in range(z.size(0)):\n","  print(torch.max(data[1,0]))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["tensor(1., device='cuda:0')\n","tensor(1., device='cuda:0')\n","tensor(1., device='cuda:0')\n","tensor(1., device='cuda:0')\n","tensor(1., device='cuda:0')\n","tensor(1., device='cuda:0')\n","tensor(1., device='cuda:0')\n","tensor(1., device='cuda:0')\n","tensor(1., device='cuda:0')\n","tensor(1., device='cuda:0')\n","tensor(1., 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device='cuda:0')\n","tensor(1., device='cuda:0')\n","tensor(1., device='cuda:0')\n","tensor(1., device='cuda:0')\n"],"name":"stdout"},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-48-c26c11657854>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/tensor.py\u001b[0m in \u001b[0;36m__repr__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     69\u001b[0m         \u001b[0;31m# characters to replace unicode characters with.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mversion_info\u001b[0m \u001b[0;34m>\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m     \u001b[0mformatter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_Formatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_summarized_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msummarize\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_tensor_str_with_formatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msummarize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m 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